Kurdistan Journal of Applied Research (KJAR)
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Real Time Intrusion Detection System Based on Web Log File Analysis
Get PDFWeb log data have a wealth of useful data about a website. They contain the history of all users’ activities while accessing websites. Some log files contain records of various intrusion types that refer to unauthorized or malicious activities recorded during website access. System and network logs are examined as part of log file analysis for Intrusion Detection Systems (IDS) to identify suspicious activities and possible security risks. Many existing IDS systems suffer from false positives and false negatives, which can either fail to identify real dangers or overwhelm administrators with unnecessary alarms. Real-time cyberattacks are common, and any delay in detection can lead to serious consequences like data breaches and system outages. In this paper, we developed a real time IDS based on weblog analysis which is used to predict if the user’s request is an attack, normal, or suspicious. This can be done by utilizing the contents of the Apache access log data, considering some of the hyper text transfer protocol request features obtained by analyzing the user’s requests. In this work, various data preprocessing techniques are applied, and key features are extracted, enhancing the system's ability to effectively detect intrusions. The model was constructed using four machine learning algorithms: gradient-boosted trees, decision tree, random forest, and support vector machine. According to the results obtained, the proposed model with the random forest algorithm produces the most accurate model among the others. It attained 99.66% precision, 99.66% recall, and 99.83% accuracy score
Formulation, Phytochemical Characterization, and Clinical Assessment of a Novel Natural Supplement Targeting Body Composition in Physically Active Individuals: A Randomized Clinical Trial
Get PDFNutritional supplementation plays a pivotal role in optimizing body composition, recovery, and performance in physically active individuals. This study aimed to evaluate the effects of an 8-week intervention with a novel natural supplement (NNS) on body composition participants. In a randomized, placebo-controlled trial, 55 participants (NNS: n = 28; placebo: n = 27) consumed either the NNS formulation comprising whey and pea protein, oats, flaxseed, spinach, beetroot, and chia or a placebo. Body composition (muscle mass, weight, BMI, fat %), oxygen saturation, and heart rate were measured at baseline and post-intervention. After 8 weeks, The NNS group showed a significant increase in muscle mass by 41.9%, rising from 12.96 kg to 18.41 kg (p = 0.000), while the placebo group only increased from 13.94 kg to 14.44 kg. Body weight in the NNS group decreased by 8.14 kg, from 76.54 kg to 68.40 kg (p < 0.001), whereas the placebo group gained 2.46 kg. BMI improved in the NNS group, dropping from 30.98 kg/m² to 25.7 kg/m² (p < 0.001), while remaining stable in the placebo group. Oxygen saturation increased from 95.85% to 98.62% (p = 0.001), and heart rate decreased from 76.00 bpm to 68.22 bpm (p = 0.004) in the NNS group. Fat percentage decreased from 30.63% to 27.11% (p = 0.0297). In conclusion, the novel natural multi-ingredient supplement significantly improved muscle mass, reduced body weight and BMI, and enhanced cardiopulmonary parameters, indicating its potential as a safe and effective nutritional strategy for improving body composition and performance in physically active individuals
Influence of Cabling on Photovoltaic System Performance: Wire Length, Diameter, and Material
Get PDFDespite advancements in solar PV technology, significant challenges remain in the Global South, including financial, human resource, environmental, and technological constraints. System losses—caused by reflection, temperature effects, inverter inefficiency, cabling losses, shading, and degradation—are a major concern. This study examines how cabling parameters—wire length, diameter, and material—affect PV system performance and energy losses. Using a computational model, it evaluates a 3 kWp PV system in Durban, South Africa, analyzing efficiency, specific annual yield, and avoidable CO₂ emissions across various cabling configurations. The study’s key findings include: at a constant wire diameter of 4 mm, specific annual yield decreases as wire length increases, dropping from 977.36 kWh/kW at 5 m to 966.32 kWh/kW at 50 m, reflecting efficiency losses; at a constant wire length of 20 m, yield improves with increasing diameter, rising from 970.71 kWh/kWp at 2.5 mm to 977.81 kWh/kWp at 20 mm. Beyond 25 mm, yield gains diminish, stabilizing around 978.39 kWh/kW at 90 mm; at a fixed wire length of 20 m, avoided CO₂ emissions increase with wire diameter up to 25 mm, after which gains level off from 30 mm to 90 mm; at a constant diameter of 4 mm, avoided CO₂ emissions increase from 1,378 kg/year at a wire length of 5 m to 1,363 kg/year at 50 m. These findings highlight the importance of optimizing cabling parameters to minimize system losses and enhance the overall efficiency and sustainability of PV systems
A Comparative Analysis of ChatGPT and Traditional Machine Learning Algorithms on Real-World Data
No full textThe rapid growth of computer-based technologies has transformed many sectors, with artificial intelligence playing a key role in automating tasks previously performed by humans. In this context, natural language processing models such as chatbots, including Chat Generative Pre-Trained Transformer (ChatGPT), are increasingly being used as analytical tools alongside traditional machine learning algorithms. However, despite these advancements, concerns remain regarding the accuracy, processing time, and overall reliability of ChatGPT compared to traditional coding-based machine learning algorithms. This study provides a comparative evaluation of ChatGPT’s ability to generate intelligent responses. It focuses on three key aspects: accuracy across various datasets at different time intervals using the same account, performance relative to traditional machine learning algorithms in terms of accuracy, and the variability of ChatGPT’s results across diverse data sources. To address these concerns, 15 algorithms were tested against ChatGPT. Tests were done at four different time intervals using healthcare and education datasets. ChatGPT showed competitive accuracy but had more variability and slower processing. As a result, this study highlights notable performance limitations for ChatGPT. For instance, in the heart disease dataset, the Random Forest model achieved an accuracy of 0.672 in 0.012 seconds, whereas the average performance of ChatGPT was 0.608 with a processing time of 0.274 seconds. In comparison, the traditional Gradient Boosting Machine model attained an accuracy of 0.623 in 0.124 seconds, while ChatGPT recorded an accuracy of 0.589 in 1.019 seconds. Finally, this study draws specific conclusions based on the results and offers recommendations for future research
Prevalence and Identification of Bacillus cereus in Some Dried Dairy Products Focuses on Its Toxigenic Genes
Get PDFBacillus cereus is a spore-forming, toxin-producing bacterium. It has significant food safety risks, especially in dried dairy products, including powdered and infant milk. This may pose a risk to the consumers. The current study aimed to show the prevalence and toxigenic potential of B. cereus in 134 milk samples. They were collected randomly from food stores and various sale points in the local markets from Sulaymaniyah and Halabja provinces between November 2024 and May 2025. Bacterial isolation was performed using Mannitol egg Yolk Polymyxin (MYP) agar. The isolates were then confirmed by biochemical assays, VITEK 2 (BCL), and Polymerase Chain Reaction (PCR) amplification of gyrB and 16S rRNA genes. Phylogenetic analysis revealed close genetic clustering of the isolates with reference strains of B. cereus, B. thuringiensis, and B. tropicus. Antimicrobial susceptibility testing using the disk diffusion method presented complete resistance to β-lactam antibiotics. However, all isolates remained susceptible to ciprofloxacin, tetracycline, erythromycin, and vancomycin. Of the tested samples, B. cereus was detected in 48% of different types of powdered milk and 11.7% of infant milk samples. Virulence gene analysis displayed high prevalence rates of enterotoxins: nheA (100%), nheB (80.76%), cytK (86.53%), hblA (75%), and hblC (88.46%). While the emetic toxin gene ces was not detected in any milk samples. In conclusion, the presenting of multi-virulent and β-lactam-resistant B. cereus in dried milk reinforce the need for improved hygiene during dairy processing. Future studies can employ whole-genome sequencing approaches to better understand the genetic diversity, and virulence mechanisms in B. cereus from dairy environments.
Suitability of Sprouted and Fresh Three Potato Cultivars for Chip Manufacture: A Chemical and Sensory Evaluation
Get PDFPotatoes are a globally important crop and a key raw material for the production of chips. Sprouting during potato storage is a critical problem that alters chemical and sensory attributes such as sugar accumulation, starch reduction, and firmness loss, which negatively affect chip quality. However, limited studies have addressed these changes across cultivars under local conditions. The present study explored the influence of fresh potato (at harvest) and sprouting stages (non-sprouted, and sprouted at 1 cm and 2 cm length) on the chemical properties and sensory quality of three potato chip cultivars (Crozo, Hermes, and Lady Rosetta), which are widely grown in the Kurdistan Region of Iraq. To ensure clarity of the experimental design, the study specifically evaluated these three cultivars across four developmental stages to assess their chemical and sensory quality for chip production. After that, the results indicated that Crozo exhibited the highest dry matter (29.78%) and starch (22.53%) at 2 cm sprouting, indicating strong frying potential. Lady Rosetta exhibited the least weight loss (5.85%) and the highest firmness (33.02 N) at harvest. TSS increased after storage, peaking in Lady Rosetta (8.67 %), while total sugar content decreased during sprouting to 0.089%. Sensory scores for chips declined with increased sprout length. Crozo and Lady Rosetta maintained a better appearance, flavor, and texture during the sprouting stages than Hermes. Overall, the cultivar and sprouting stages had a significant influence on quality. Fresh and non-sprouted potatoes are recommended for optimal chip quality. The results concluded that Crozo and Lady Rosetta cultivars are most suitable for storage conditions and processing due to their favorable chemical traits and sensory performance
Soybean (Glycine max L.) Yield and Yield Component Under Different Planting Time and Foliar Application of Humic Acid
Get PDFA triplicate field experiment laid out in randomized complete block design was conducted to evaluate the effect of foliar application of humic acid rates (HAR) at Bakrajo, Sulaimani which is (located at 35°32'52.8"N and 45°21'16.6"E) belongs to Kurdistan region of Iraq with the silty clay soil type. Five different Humic Acid rates (HAR) which were (0 (control), 2,4,6,8 g/L) and two cultivation period (CP) which were cultivation Period 1 (CP1) on 15 May 2022 and cultivation Period 2 (CP2) on 1st of June 2022. The effect of foliar Humic acid rates application methods was highly significant on the Pod No./ Plant, Empty Pod/Plant, Seed Weight / Plant (g) and yield (kg/ha) and significant on the 1000 Seed Weight (g). While, the effect of cultivation period (CP) was highly significant only for the Pod No./ Plant and the insignificantly affected the other parameters including Empty Pod/Plant, Seed No./ Pod, 1000 Seed Weight (g), Weight / Plant (g) and yield (kg/ha). The maximum Pod No./ Plant, Empty Pod/Plant, Seed Weight / Plant (g) and yield (kg/ha) and significant on the 1000 Seed Weight (g) were observed under foliar application of HAR of 6 g/L and the best cultivation period for sowing humic acid was (CP1) which was on 15 May
A Wavelet Shrinkage Mixed with a Single-level 2D Discrete Wavelet Transform for Image Denoising
Get PDFThe single-level 2D discrete wavelet transform method is a powerful technique for effectively removing Gaussian noise from natural images. Its effectiveness is attributed to its ability to capture a signal's energy at low energy conversion values, allowing for efficient noise reduction while preserving essential image details. The wavelet noise reduction method mitigates the noise present in the waveform coefficients produced by the discrete wavelet transform. In this study, three different wavelet families—Daubechies (db7), Coiflets (coif5), and Fejér-Korovkin (fk4)—were evaluated for their noise removal capabilities using the Bayes shrink method. This approach was applied to a set of images, and the performance was analyzed using the Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) metrics. Our results demonstrated that among the wavelet families tested, the Fejér-Korovkin (fk4) wavelet consistently outperformed the others. The fk4 wavelet family yielded the lowest MSE values, indicating minimal reconstruction error, and the highest PSNR values, reflecting superior noise suppression and better image quality across all tested images. These findings suggest that the fk4 wavelet family, when combined with the Bayes shrink method, provides a robust framework for Gaussian noise reduction in natural images. The comparative analysis highlights the importance of selecting appropriate wavelet families to optimize noise reduction performance, paving the way for further research and potential improvements in image denoising techniques
A Comparative Study Evaluated the Performance of Two-class Classification Algorithms in Machine Learning
Get PDFWorldwide, heart attacks, also called myocardial infarctions, are a leading cause of death. Early detection and accurate prediction of heart attacks are crucial for effective medical intervention and patient care. In recent years, machine learning techniques have shown great promise in aiding the diagnosis and prediction of heart attacks. The Organization for World Health (WHO) reports that around 17 million individuals worldwide pass away from cardiovascular diseases (CVD), notably heart attacks and strokes, each year. In this study, 1026 patients, both men and women, are almost equally affected by CVDs. While heart attacks and strokes remain among the leading causes of mortality worldwide, the use of machine learning for predicting heart disease could potentially prevent premature deaths. A comparative study evaluated the performance of five well-known two-class classification algorithms: two-class boosted decision trees, two-class decision forests, two-class locally deep SVMs, two-class neural networks, and two-class logistic regression. Among these algorithms, the Two-Class Boosted Decision Tree method demonstrated outstanding prediction ability, achieving a 100% accuracy rating. Its exceptional recall and precision rates highlight its effectiveness in handling challenging classifications. To facilitate the development and deployment of machine learning models, Azure Machine Learning offers a range of tools and services. By leveraging Azure Machine Learning's capabilities, researchers and healthcare professionals can analyze large datasets containing patient information and medical records to identify patterns and risk factors associated with heart attacks
Multi-Label Feature Selection with Graph-based Ant Colony Optimization and Generalized Jaccard Similarity
Get PDFMulti-label learning is a technique that assigns multiple class labels to each data instance. The growth of digital technology resulted in the development of high-dimensional applications in real-world scenarios. Feature selection approaches are extensively used to reduce dimensionality in multi-label learning. The main problems of the recommender system are determining the best match of futures among users but have not engaged with previously. This paper proposes a strategy for selecting features using ant colony optimization (ACO) that incorporates mutual knowledge. The proposed method utilizes ACO to rank features based on their significance. Thus, the search space is mapped to a graph, and each ant traverses the graph, selecting a predetermined number of features. A new information-theoretical metric is introduced to evaluate the features chosen by each ant. Jaccard generalized similarity coefficient is used to select the most suitable communication target for efficient learning outcomes. Mutual information is employed to assess each features relevance to a set of labels and identify redundant features. Pheromones are assigned values based on the effectiveness of the ants in solving the problem. Finally, the features are ranked based on their pheromone values, and the top-ranked features are selected as the final set of attributes. The proposed method is evaluated using real-world datasets. The findings demonstrate that the proposed method outperforms most of existing and advanced approaches. This paper presents a novel feature selection approach for multi-label learning based on ACO. The experimental results confirm the effectiveness of the proposed method compared to existing techniques