Twitter popularity has increasingly grown in the last few years, influencing life’s social, political, and business aspects. People would leave their tweets on social media about an event, and simultaneously inquire to see other people's experiences and whether they had a positive/negative opinion about that event. Sentiment Analysis can be used to obtain this categorization. Product reviews, events, and other topics from all users that comprise unstructured text comments are gathered and categorized as good, harmful, or neutral using sentiment analysis. Such issues are called polarity classifications. This study aims to use Twitter data about OK cuisine reviews obtained from the Amazon website and compare the effectiveness

     Optimization is the task of minimizing or maximizing an objective function f(x) parameterized by x. A series of effective numerical optimization methods have become popular for improving the performance and efficiency of other methods characterized by high-quality solutions and high convergence speed. In recent years, there are a lot of interest in hybrid metaheuristics, where more than one method is ideally combined into one new method that has the ability to solve many problems rapidly and efficiently. The basic concept of the proposed method is based on the addition of the acceleration part of the Gravity Search Algorithm (GSA) model in the Firefly Algorithm (FA) model and creating new individuals. Some stan

Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has

<p><span>A Botnet is one of many attacks that can execute malicious tasks and develop continuously. Therefore, current research introduces a comparison framework, called BotDetectorFW, with classification and complexity improvements for the detection of Botnet attack using CICIDS2017 dataset. It is a free online dataset consist of several attacks with high-dimensions features. The process of feature selection is a significant step to obtain the least features by eliminating irrelated features and consequently reduces the detection time. This process implemented inside BotDetectorFW using two steps; data clustering and five distance measure formulas (cosine, dice, driver &amp; kroeber, overlap, and pearson correlation

ABSTRACT:
Microencapsulation is used to modify and retard drug release as well as to overcome the unpleasant effect
(gastrointestinal disturbances) which are associated with repeated and overdose of ibuprofen per day.
So that, a newly developed method of microencapsulation was utilized (a modified organic method) through a
modification of aqueous colloidal polymer dispersion method using ethylcellulose and sodium alginate coating materials to
prepare a sustained release ibuprofen microcapsules.
The effect of core : wall ratio on the percent yield and encapsulation efficiency of prepared microcapsules was low, whereas
, the release of drug from prepared microcapsules was affected by core: wall ratio ,proportion of coa

Background Subtraction (BGS) is one of the main techniques used for moving object detection which further utilized in video analysis, especially in video surveillance systems. Practically, acquiring a robust background (reference) image is a real challenge due to the dynamic change in the scene. Hence, a key point to BGS is background modeling, in which a model is built and repeatedly used to reconstruct the background image.

From N frames the proposed method store N pixels at location(x,y) in a buffer, then it classify pixel intensity values at that buffer using a proposed online clustering model based on the idea of relative  run length, the cluster center with the highest frequency will be adopted as the background pixel

This research deals with processing and Interpretation of Bouguer anomaly gravity field, using two dimensional filtering techniques to separate the residual gravity field from the Bouguer gravity map for a part of Najaf Ashraf province in Iraq. The residual anomaly processed in order to reduce noise and give a more comprehensive vision about subsurface linear structures. Results for descriptive interpretation presented as colored surfaces and contour maps in order to locate directions and extensions of linear features which may interpret as faults. A comparison among gravity residual field , 1st derivative and horizontal gradient made along a profile across the study area in order to assign the exact location of a major fault. Furthermor

Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems.  Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic.  Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance.  In this study, two different sets of select

Due to the lack of vehicle-to-infrastructure (V2I) communication in the existing transportation systems, traffic light detection and recognition is essential for advanced driver assistant systems (ADAS) and road infrastructure surveys. Additionally, autonomous vehicles have the potential to change urban transportation by making it safe, economical, sustainable, congestion-free, and transportable in other ways. Because of their limitations, traditional traffic light detection and recognition algorithms are not able to recognize traffic lights as effectively as deep learning-based techniques, which take a lot of time and effort to develop. The main aim of this research is to propose a traffic light detection and recognition model based on

Deep learning techniques are applied in many different industries for a variety of purposes. Deep learning-based item detection from aerial or terrestrial photographs has become a significant research area in recent years. The goal of object detection in computer vision is to anticipate the presence of one or more objects, along with their classes and bounding boxes. The YOLO (You Only Look Once) modern object detector can detect things in real-time with accuracy and speed.&nbsp; A neural network from the YOLO family of computer vision models makes one-time predictions about the locations of bounding rectangles and classification probabilities for an image. In layman's terms, it is a technique for instantly identifying and recognizing