• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.101-106
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• 2021

Breast cancer is the number one cause of deaths from cancer in women, knowing the type of breast cancer in the early stages can help us to prevent the dangers of the next stage. The performance of the deep learning depends on large number of labeled data, this paper presented convolutional neural network for classification breast cancer from images to benign or malignant. our network contains 11 layers and ends with softmax for the output, the experiments result using public BreakHis dataset, and the proposed methods outperformed the state-of-the-art methods.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.32-38
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• 2023

Blackboard provides a collaborative environment for teaching in terms of assessment and communication and can improve learning outcomes. To evaluate the Blackboard use of faculty members at Umm Al-Qura University, data were collected from two channels: statistical reports issued by the university and an online questionnaire. The questionnaire survey respondents were 187 faculty members from all colleges in the university. The findings show that most faculty members did not use Blackboard before the pandemic; therefore, the sudden conversion to the use of Blackboard required intensive training courses. In addition, accompanying Blackboard use with other applications such as WebEx is preferable, especially for administrative tasks such as departmental board meetings and seminars.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.51-59
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• 2024

In this work, we focused on reducing the amount of image data to be sent by extracting and progressively sending prominent image features to high-performance computing systems taking into consideration the right amount of image data required by object identification application. We demonstrate that with our technique called Progressive Object Detection over a Lossless Network using Fragmented DCT Coefficients (Proficient), object identification applications can detect objects with at least 70% combined confidence level by using less than half of the image data.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.11-20
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• 2024

While artificial neural networks are adept at identifying patterns, they can struggle to distinguish between actual correlations and false associations between extracted facial features and criminal behavior within the training data. These associations may not indicate causal connections. Socioeconomic factors, ethnicity, or even chance occurrences in the data can influence both facial features and criminal activity. Consequently, the artificial neural network might identify linked features without understanding the underlying cause. This raises concerns about incorrect linkages and potential misclassification of individuals based on features unrelated to criminal tendencies. To address this challenge, we propose a novel region-based training approach for artificial neural networks focused on criminal propensity detection. Instead of solely relying on overall facial recognition, the network would systematically analyze each facial feature in isolation. This fine-grained approach would enable the network to identify which specific features hold the strongest correlations with criminal activity within the training data. By focusing on these key features, the network can be optimized for more accurate and reliable criminal propensity prediction. This study examines the effectiveness of various algorithms for criminal propensity classification. We evaluate YOLO versions YOLOv5 and YOLOv8 alongside VGG-16. Our findings indicate that YOLO achieved the highest accuracy 0.93 in classifying criminal and non-criminal facial features. While these results are promising, we acknowledge the need for further research on bias and misclassification in criminal justice applications

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.53-63
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• 2024

Alzheimer's disease is a brain disorder that worsens over time and affects millions of people around the world. It leads to a gradual deterioration in memory, thinking ability, and behavioral and social skills until the person loses his ability to adapt to society. Technological progress in medical imaging and the use of artificial intelligence, has provided the possibility of detecting Alzheimer's disease through medical images such as magnetic resonance imaging (MRI). However, Deep learning algorithms, especially convolutional neural networks (CNNs), have shown great success in analyzing medical images for disease diagnosis and classification. Where CNNs can recognize patterns and objects from images, which makes them ideally suited for this study. In this paper, we proposed to compare the performances of Alzheimer's disease detection by using two deep learning methods: You Only Look Once (YOLO), a CNN-enabled object recognition algorithm, and Visual Geometry Group (VGG16) which is a type of deep convolutional neural network primarily used for image classification. We will compare our results using these modern models Instead of using CNN only like the previous research. In addition, the results showed different levels of accuracy for the various versions of YOLO and the VGG16 model. YOLO v5 reached 56.4% accuracy at 50 epochs and 61.5% accuracy at 100 epochs. YOLO v8, which is for classification, reached 84% accuracy overall at 100 epochs. YOLO v9, which is for object detection overall accuracy of 84.6%. The VGG16 model reached 99% accuracy for training after 25 epochs but only 78% accuracy for testing. Hence, the best model overall is YOLO v9, with the highest overall accuracy of 86.1%.

• Advances in environmental research
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• v.3 no.1
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• pp.11-28
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• 2014

Advanced oxidation processes using UV and catalysts like $TiO_2$ and ZnO have been recently applied for the photocatalytic degradation of MTBE in water. Attempts have been made to replace the UV radiation by the solar spectrum. This review intends to shed more light on the work that has been done so far in this area of research. The information provided will help in crystallizing the ideas required to shift the trend from UV photocatalysis to sunlight photocatalysis. The careful optimization of the reaction parameters and the type of the dopant employed are greatly responsible for any enhancement in the degradation process. The advantage of shifting from UV photocatalysts to visible light photocatalysts can be observed when catalysts like $TiO_2$ and ZnO are doped with suitable metals. Therefore, it is expected that in the near future, the visible light photocatalysis will be the main technique applied for the remediation of water contaminated with MTBE.

• Asian-Australasian Journal of Animal Sciences
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• v.8 no.5
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• pp.443-447
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• 1995

Fifteen Karan-Swiss male calves of 9-12 months of age were divided into three groups of five each in a randomised block design. Animals in group I were fed wheat straw ad lib. and concentrate mixture according to their requirements, while in group II and III the animals were fed with 1/3 of the required concentrate mixture replaced by UMM licks 'Ex' and 'F', respectively. The DMI (kg/day as well as per 100 kg b.wt.) were similar (p > 0.05) among different groups of animals, however, the digestibility of DM as well as OM enhanced from $52.85{\pm}1.48$ to $58.36{\pm}1.89$ and $55.33{\pm}1.48$ to $60.12{\pm}1.75$, respectively. Growth rates of the calves were $533.8{\pm}27.25$, $532.3{\pm}42.24$ and $538.4{\pm}18.68$ g/d in groups I, II and ill (p > 0.05), respectively. Body composition and N balances of the animals were not affected by supplementation of UMM licks, however, protein retention efficiency was higher in group III ($82.57{\pm}2.54$) though nonsignificant. Feed cost/day was reduced from Rs. 7.92 (group I) to Rs. 4.62 (group II) and Rs. 3.44 (group III). Hence, partial replacement of concentrates by UMM licks reduced the cost of feeding of growing calves by 41.7 to 56.6% without affecting the growth performance.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.65-70
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• 2021

Waste accumulation is becoming a significant challenge in most urban areas and if it continues unchecked, is poised to have severe repercussions on our environment and health. The massive industrialisation in our cities has been followed by a commensurate waste creation that has become a bottleneck for even waste management systems. While recycling is a viable solution for waste management, it can be daunting to classify waste material for recycling accurately. In this study, transfer learning models were proposed to automatically classify wastes based on six materials (cardboard, glass, metal, paper, plastic, and trash). The tested pre-trained models were ResNet50, VGG16, InceptionV3, and Xception. Data augmentation was done using a Generative Adversarial Network (GAN) with various image generation percentages. It was found that models based on Xception and VGG16 were more robust. In contrast, models based on ResNet50 and InceptionV3 were sensitive to the added machine-generated images as the accuracy degrades significantly compared to training with no artificial data.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.55-64
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• 2021

In the last few years, the massive development in wireless networks, high internet speeds and improvement in car manufacturing has shifted research focus to Vehicular Ad-HOC Networks (VANETs). Consequently, many related frameworks are explored, and it is found that security is the primary issue for VANETs. Despite that, a small number of research studies have taken into consideration the identification of performance standards and parameters. In this paper, VANET security frameworks are explored, studied and analysed which resulted in the identification of a list of performance evaluation parameters. These parameters are defined and categorized based on the nature of parameter (security or general context). These parameters are identified to be used by future researchers to evaluate their proposed VANET security frameworks. The implementation paradigms of security frameworks are also identified, which revealed that almost all research studies used simulation for implementation and testing. The simulators used in the simulation processes are also analysed. The results of this study showed that most of the surveyed studies used NS-2 simulator with a percentage of 54.4%. The type of scenario (urban, highway, rural) is also evaluated and it is found that 50% studies used highway urban scenario in simulation.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.182-190
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• 2021

Deep learning is an advanced technology for large-scale data analysis, with numerous promising cases like image processing, object detection and significantly more. It becomes customarily to use transfer learning and fine-tune a pre-trained CNN model for most image recognition tasks. Having people taking photos and tag themselves provides a valuable resource of in-data. However, these tags and labels might be noisy as people who annotate these images might not be experts. This paper aims to explore the impact of noisy labels on fine-tuning pre-trained CNN models. Such effect is measured on a food recognition task using Food101 as a benchmark. Four pre-trained CNN models are included in this study: InceptionV3, VGG19, MobileNetV2 and DenseNet121. Symmetric label noise will be added with different ratios. In all cases, models based on DenseNet121 outperformed the other models. When noisy labels were introduced to the data, the performance of all models degraded almost linearly with the amount of added noise.