The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our system

Breast cancer was one of the most common reasons for death among the women in the world. Limited awareness of the seriousness of this disease, shortage number of specialists in hospitals and waiting the diagnostic for a long period time that might increase the probability of expansion the injury cases. Consequently, various machine learning techniques have been formulated to decrease the time taken of decision making for diagnoses the breast cancer and that might minimize the mortality rate. The proposed system consists of two phases. Firstly, data pre-processing (data cleaning, selection) of the data mining are used in the breast cancer dataset taken from the University of California, Irvine machine learning repository in this stage we

  The Silver¹Indium¹Selenide (AgInSe₂) (AIS) thin¹films of (300¹Â±20) nm thickness  have been¹prepared²from the compound alloys²using thermal evaporation² technique onto the glass²substrate at room temperature, with a deposition rate²(3±0.1) nm²sec^-1.

The²structural, optical and electrical³properties have been studied³at different annealing³temperatures (Ta=450, 550 and 650) K.

The amount³or (concentration) of the elements³(Ag, In, Se) in the  prepared alloy³was verified using  an

Sb-dopedAgInSe2 (AIS: 3%Sb)thin films were synthesized by thermal evaporation with a vacuum of 7*10^-6torr on glass with (400+20) nm thickness. X-ray diffraction was used to show that Sb atoms were successfully incorporated into the AgInSe2 lattice. Then the thin films are annealed in air at 573 K. XRD shows that thin films AIS pure, AIS: 3%Sb and annealing at 573 K are polycrystalline with tetragonal structure with preferential orientation (112).raise the crystallinity degree. The Absorption spectra revealed that the average Absorption was more than 60% at the wavelength range of 400–700 nm. UV/Visible measure shows the lowering in energy gap to 1.4 eV forAIS: 3%Sb at 573 Kt his energy gap making these samples suitable for p

Due to the increased of information existing on the World Wide Web (WWW), the subject of how to extract new and useful knowledge from the log file has gained big interest among researchers in data mining and knowledge discovery topics.
Web miming, which is a subset of data mining divided into three particular ways, web content mining, web structure mining, web usage mining. This paper is interested in server log file, which is belonging to the third category (web usage mining). This file will be analyzed according to the suggested algorithm to extract the behavior of the user. Knowing the behavior is coming from knowing the complete path which is taken from the specific user.
Extracting these types of knowledge required many of KDD

Texture is an important characteristic for the analysis of many types of images because it provides a rich source of information about the image. Also it provides a key to understand basic mechanisms that underlie human visual perception. In this paper four statistical feature of texture (Contrast, Correlation, Homogeneity and Energy) was calculated from gray level Co-occurrence matrix (GLCM) of equal blocks (30×30) from both tumor tissue and normal tissue of three samples of CT-scan image of patients with lung cancer. It was found that the contrast feature is the best to differentiate between textures, while the correlation is not suitable for comparison, the energy and homogeneity features for tumor tissue always greater than its valu

The interests toward developing accurate automatic face emotion recognition methodologies are growing vastly, and it is still one of an ever growing research field in the region of computer vision, artificial intelligent and automation. However, there is a challenge to build an automated system which equals human ability to recognize facial emotion because of the lack of an effective facial feature descriptor and the difficulty of choosing proper classification method. In this paper, a geometric based feature vector has been proposed. For the classification purpose, three different types of classification methods are tested: statistical, artificial neural network (NN) and Support Vector Machine (SVM). A modified K-Means clustering algorithm

In the image processing’s field and computer vision it’s important to represent the image by its information. Image information comes from the image’s features that extracted from it using feature detection/extraction techniques and features description. Features in computer vision define informative data. For human eye its perfect to extract information from raw image, but computer cannot recognize image information. This is why various feature extraction techniques have been presented and progressed rapidly. This paper presents a general overview of the feature extraction categories for image.

The fact that the signature is widely used as a means of personal verification
emphasizes the need for an automatic verification system. Verification can be
performed either Offline or Online based on the application. Offline systems work on
the scanned image of a signature. In this paper an Offline Verification of handwritten
signatures which use set of simple shape based geometric features. The features used
are Mean, Occupancy Ratio, Normalized Area, Center of Gravity, Pixel density,
Standard Deviation and the Density Ratio. Before extracting the features,
preprocessing of a scanned image is necessary to isolate the signature part and to
remove any spurious noise present. Features Extracted for whole signature

     Measuring goat weight on a farm is carried out by two methods. The first method conventionally measures the chest girth (CG) and body length (BL), while the second is performed using a scale. This study aimed to facilitate the CG and BL measurements to estimate goat weight more efficiently through image processing. The endpoints of CG and BL were determined automatically using binary large object (BLOB) segmentation. The images of 120 goats were taken at three different shooting distances of 50 cm, 70 cm, and 90 cm. The statistical testing of the three scenarios in this study explained that the ideal distance to take pictures is the basis for determining whether or not the size of the circumference and BL is appropriate as a fea