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A Hybrid Genetic Algorithm-Particle Swarm Optimization Approach for Enhanced Text Compression
Full text linkText compression is a necessity for efficient data storage and transmission. Especially in the digital era, volumes of digital text have increased incredibly. Traditional text compression methods, including Huffman coding and Lempel-Ziv-Welch, have certain limitations regarding their adaptability and efficiency in dealing with such complexity and diversity of data. In this paper, we propose a hybrid method that combines Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) to optimize the compression of text using the broad exploration capabilities of GA and fast convergence properties of PSO. The experimental results reflect that the proposed hybrid approach of GA-PSO yields much better performance in compression ratio than the standalone methods by reducing the size to about 65% while retaining integrity in the original content. The proposed method is also highly adaptable to various text forms and outperformed other state-of-the-art methods such as the Grey Wolf Optimizer, the Whale Optimization Algorithm, and the African Vulture Optimization Algorithm. These results support that the hybrid method GA-PSO seems promising for modern text compression
Enhancing COVID-19 Detection Accuracy: Optimal Gene Combinations, Kit Performance, and Reliable Detection Intervals
Full text linkA significant challenge and threat to public health have been created by the COVID-19 pandemic for the entire global population. The study aimed to compare the SARS-CoV-2 RNA detection capabilities of available primers and probes to identify the most reliable, efficient, and affordable method. From 200 previously detected samples of SARS CoV-2, 94 samples were selected randomly and used for the optimization of our primers and probes. We compared our results with two kits that have been approved by the health authority. In addition, we evaluated the detectability of each gene. The study compared the diagnostic performance of different gene combinations for COVID-19 detection using kits A and B and a novel approach combining RdRp, N, and E genes. Results showed that the combined approach exhibited superior discriminatory power, particularly with the inclusion of the E gene, boasting area under the curve (AUC) values of 83.3%, 79.1%, and 93.7% for the respective genes. Kit B, with Orf1ab and N genes, outperformed Kit A (RdRp and S genes), with AUC values of 81.2% and 90.6% versus 80.2% and 75%, respectively. The chart representation highlighted gene detection frequencies across various cycle threshold (Ct) ranges, demonstrating robust identification within the 20.1–30 Ct range across all kits and genes, emphasizing the reliability of detection within specific intervals. Combining RdRp, N, and E genes showed the highest accuracy for COVID-19 diagnosis, particularly with the E gene. Detection was most reliable within the 20.1–30 Ct range across all gene combinations and kits
Prediction of Lung Cancer Disease Using Machine Learning Techniques
Full text linkThe pursuit of algorithms utilizing external examples to formulate extensive hypotheses predicting the occurrence of novel instances is recognized, as supervised machine learning (SML). One of the jobs that intelligent systems perform the most frequently is supervised classification. The goal of this work is to evaluate supervised learning algorithms, explain SML classification methodologies, and identify the most effective classification algorithm given the available data. Two distinct machine learning (ML) techniques were examined: Random Forest (RF) and Neural Networks (NN). The algorithms were implemented using Python for knowledge analysis. For the categorization, 310 cases from a lung cancer data set were employed, with 15 features serving as independent variables and one serving as the dependent variable. In comparison to NN classification methods, RF was found to be the algorithm with the highest precision and accuracy, according to the results. The study reveals that while the kappa statistic and mean square error (MSE) are factors on the one hand, the time required to create a model and precision (accuracy) are factors on the other. Consequently, to have supervised predictive ML algorithms need to be precise, accurate, and minimum error. Thus, as a consequence of the research, we are currently at this analysis. The categorizing of NNs accuracy is 0.75 the MSE is 0.25, The RF classification accuracy is 0.89 and the MSE is 0.21
The Role of Nursing Educational Program in Improving Nutritional Status among Patients with Heart Failure
Full text linkBackground: Heart failure (HF) is characterized by cardiac dysfunction, fluid retention, and subsequent exercise intolerance, significantly impacting patients’ quality of life. Nutrition plays a pivotal role in managing HF, as dietary modifications can alleviate symptoms and improve clinical outcomes. Foods are categorized based on their nutritional density, which is critical for optimizing health in HF patients. Nurses play a key role in healthcare by providing education, empowering patients to make informed decisions, and enhancing healthcare efficiency through structured interventions. Methods: From (August 2 to February 8, 2024), 200 patients with HF were treated in the Sulaimani Cardiac Hospital in Sulaimani City using a quasi-experimental technique. The participants were divided into two groups: Intervention (100) and control (100). They completed a detailed questionnaire that included demographics, medical data, and a variety of examinations. The interventional group had exclusive access to the nutrition education program. Data were collected through direct interviews and processed with the Statistical Package for the Social Sciences, version (26). Results: The average age of the respondents was (68.3 ± 11.2) years. At first, both groups comprised (100) patients, however, the intervention group dropped to (94) and the control group to (90) owing to fatalities. The nutrition education program resulted in significant differences between the groups in high-density lipoprotein and cholesterol levels, blood electrolytes, waist-hip ratios, and hospitalization rates. Conclusions and recommendations: The intervention group showed greater improvements in nutritional status than the control group. A 12-week educational program improved eating habits, reduced hypertension, diabetes, cholesterol levels, and body weight, and increased awareness of healthier food alternatives. This demonstrates that such programs can enhance quality of life, and dietary habits, and perhaps reduce death rates in the intervention group compared to the control group
Comparative Analysis of Word Embeddings for Multiclass Cyberbullying Detection
Full text linkCyberbullying has emerged as a pervasive concern in modern society, particularly within social media platforms. This phenomenon encompasses employing digital communication to instill fear, threaten, harass, or harm individuals. Given the prevalence of social media in our lives, there is an escalating need for effective methods to detect and combat cyberbullying. This paper aims to explore the utilization of word embeddings and to discern the comparative effectiveness of trainable word embeddings, pre-trained word embeddings, and fine-tuned language models in multiclass cyberbullying detection. Distinguishing from previous binary classification methods, our research delves into nuanced multiclass detection. The exploration of word embeddings holds significant promise due to its ability to transform words into dense numerical vectors within a high-dimensional space. This transformation captures intricate semantic and syntactic relationships inherent in language, enabling machine learning (ML) algorithms to discern patterns that might signify cyberbullying. In contrast to previous research, this work delves beyond primary binary classification and centers on the nuanced realm of multiclass cyberbullying detection. The research employs diverse techniques, including convolutional neural networks and bidirectional long short-term memory, alongside well-known pre-trained models such as word2vec and bidirectional encoder representations from transformers (BERT). Moreover, traditional ML algorithms such as K-nearest neighbors, Random Forest, and Naïve Bayes are integrated to evaluate their performance vis-à-vis deep learning models. The findings underscore the promise of a fine-tuned BERT model on our dataset, yielding the most promising results in multiclass cyberbullying detection, and achieving the best-recorded accuracy of 85% on the dataset
A Hybrid Artificial Bee Colony and Artificial Fish Swarm Algorithms for Software Cost Estimation
Full text linkSoftware cost estimation (SCE), estimating the cost and time required for software development, plays a highly significant role in managing software projects. A somewhat accurate SCE is necessary for a software project to be successful. It allows effective control of construction time and cost. In the past few decades, various models have been presented to evaluate software projects, including mathematical models and machine learning algorithms. In this paper, a new model based on the hybrid of the artificial fish swarm algorithm (AFSA) and the artificial bee colony (ABC) algorithm is presented for SCE. The initial population of AFSA, which includes the values of the effort factors, is generated using the ABC algorithm. ABC algorithm is used to solve the problems of the AFSA algorithm such as population diversity and getting stuck in a local optimum. ABC algorithm achieves the best solutions using observer and scout bees. The evaluation of the combined method has been implemented on eight different data sets and evaluated based on eight different criteria such as mean magnitude of relative error and PRED (0.25). The proposed method is more error-free than current SCE methods, according to the results. The error value of the proposed method is lower on NASA60, NASA63, and NASA93 datasets
Construction of Alphabetic Character Recognition Systems: A Review
Full text linkCharacter recognition (CR) systems were attracted by a massive number of authors’ interest in this field, and lot of research has been proposed, developed, and published in this regard with different algorithms and techniques due to the great interest and demand of raising the accuracy of the recognition rate and the reliability of the presented system. This work is proposed to provide a guideline for CR system construction to afford a clear view to the authors on building their systems. All the required phases and steps have been listed and clarified within sections and subsections along with detailed graphs and tables beside the possibilities of techniques and algorithms that might be used, developed, or merged to create a high-performance recognition system. This guideline also could be useful for readers interested in this field by helping them extract the information from such papers easily and efficiently to reach the main structure along with the differences between the systems. In addition, this work recommends to researchers in this field to comprehend a specified categorical table in their work to provide readers with the main structure of their work that shows the proposed system’s structural layout and enables them to easily find the information and interests
Identification of Blood Protozoa Infestation Transmitted by Vector Tikes among Awassi Sheep Herds in Kifri City, Kurdistan Region of Iraq
Full text linkBlood protozoan disease is a common disease among animals in the Kifri city, Kurdistan region of Iraq that this disease is mostly transmitted by ticks. Therefore, the present study aimed to investigate the level of blood protozoan and to identify vector ticks in the native breed sheep (Awassi sheep) in Kifri city. For this purpose, blood samples were taken from 150 sheep suspected suffering from protozoan infection according to their clinical symptoms. In the present study, we prepared blood slides from suspected sheep and stained with Giemsa staining, and then at the same time, hard ticks were collected from the sheep’s body. Then, the protozoan type was diagnosed and the vector tick species were identified by microscopically. The obtained results were statistically analyzed by the chi-square test. The results showed that 35 (23.33%) of that samples were infected with Babesia protozoa as 25 samples (16.66%) were infected with Babesia ovis, seven samples (4.66%) with Babesia mutasi, and three samples (2%) with B. ovis and B. mutasi. No infestation with Theileria and Anaplasma species was found. Rhipicephalus, Hyalomma, Dermacentor, and Haemaphysalis ticks were isolated and identified from the studied sheep. The results showed that the presence of the Rhipicephalus bursa tick is significantly (P < 0.05) related to the existence of Babesiosis disease in sheep. This study concluded that most of the studied sheep in Kifri city are infected with Babesia protozoa, especially B. ovis
Liver Fluke Species Identification Isolated From Humans and Animals Using PCR-RFLP and DNA Sequencing
Full text linkFasciola species are a member of flatworms belonging to the trematodes (flukes), commonly known as liver fluke, they are extremely pathogenic parasites that affect the liver of humans and animals, nowadays, most laboratories and research facilities use molecular-based techniques for identifying and describing Fasciola species. The molecular diagnostic markers such as polymerase chain reaction (PCR), restriction fragment length polymorphism (RFLP), and PCR-RFLP methods are accurate and more specific than the immunological and microscopical methods. The identification of the species of liver flukes will give a new clue for the treatment and control of fascioliasis. The aim, of this study, is to find the molecular characterization of Fasciola spp. isolated from humans and animals in Sulaimani city. The flukes were isolated from humans using endoscopic techniques and from slaughtered livestock at the new slaughterhouse of Sulaimani, 48 liver flukes were collected from different hosts; human (n = 3), cattle (n = 20), sheep (n = 20), and goats (n = 5) from October 2021 to April 2022. The uinversal primers ribosomal Deoxy Ribo Nuclic Acid (rDNA) were used, then the PCR products were subjected to restriction fragment polymorphism (RFLP) assay and The PCR Product was digested with restriction enzymes DraII, also the DNA sequencing was used for the PCR product of the primer Cytochrome Oxidase subuint 1 (COX1). The results of the PCR-RFLP of the 28s rDNA show the genetic polymorphisms among the flukes and two patterns of RFLP were observed F. hepatica, and F. gigantica, also the sequence analysis of the partial gene of the COX1 showed the isolated flukes belonged to F. hepatica and F. gigantica with some genetic variation, and the result of the sequences was deposited in the Gene Bank under the following Accession numbers; F. gigantica (OP718780 and OP718781) and F. hepatica (OP718782, OP718783, and OP718784). The present study concludes thatF. hepatica and F. gigantica are both responsible for human and animal Fasciolasis in Kurdistan-Iraq, Therefore, RFLP techniques and DNA sequencing are a reliable, and differential method for species and genotyping identification of liver fluke
Limitations of Load Balancing and Performance Analysis Processes and Algorithms in Cloud Computing
Full text linkIn the modern IT industry, cloud computing is a cutting-edge technology. Since it faces various challenges, the most significant problem of cloud computing is load balancing, which degrades the performance of the computing resources. In earlier research studies, the management of the workload to address all resource allocation challenges that caused by the participation of a large number of users has received important attention. When several people are attempting to access a given web application at once, managing all of those users becomes exceedingly difficult. One of the elements affecting the performance stability of cloud computing is load balancing. This article evaluates and discusses load balancing, the drawbacks of the numerous methods that have been suggested to distribute load among nodes, and the variables that are taken into account when determining the best load balancing algorithm