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Crimes Tweet Detection Based on CNN Hyperparameter Optimization Using Snake Optimizer 7th

Crimes Tweet Detection Based on CNN Hyperparameter Optimizat...

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7th National Conference on New Trends in Information and Communications Technology Applications, NTICT 2023

作者： Abdalrdha, Zainab Khyioon Al-Bakry, Abbas Mohsin Farhan, Alaa K. Informatics Institute of Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq University of Information Technology and Communications Baghdad Iraq Department of Computer Sciences University of Technology Baghdad Iraq

ISBN: (纸本)9783031628139

The increased use of social media has transformed how people share information. In the voluminous social media content, tweets involving criminal activity have increased. The detection and tracking of crime-related tweets helps law enforcement, researchers, and legislators protect the public and identify trends. This study uses Natural Language Processing (NLP) and machine learning (ML) algorithms to identify illicit tweets related to theft, violence, cybercrime, and drug use. Deep convolutional neural networks (CNNs) are used to classify these tweets, but their ideal structure requires further study. The study uses CNN hyperparameters to select relevant features from a dataset, improving model performance. The embedding dimension, dense unit count, learning rate, and batch size are considered. Snake optimization algorithms, inspired by natural processes, are used to provide optimal solutions for complex situations. The paper introduces CNN-SO, a framework that uses a snake optimization algorithm for CNN feature selection of hyperparameter optimization and improving Arabic Twitter crime identification. The model detects crime-related tweets and the results show that CNN-snake optimization outperformed the Conventional CNN used, as the accuracy in Dataset 1 was 99.57% after optimization, and 99.65% with CNN-snake optimization when using Dataset 2, which was built for this model after optimization. However, the accuracy is 88% when using Dataset 1 before optimization CNN, and 87.63% when using Dataset 2, aiming to reduce the burden of these tweets and improve public safety. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Crime

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Implementing a Novel Low Complexity CNN Model for Brain Tumor Detection 8

Implementing a Novel Low Complexity CNN Model for Brain Tumo...

引用

8th International Conference on Contemporary Information Technology and Mathematics, ICCITM 2022

作者： Al-Ani, Nawras Al-Shamma, Omran Information Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq University of Information Technology and Communications Scientific Affairs Department Baghdad Iraq

ISBN: (纸本)9798350334869

All over the world, thousands of people die yearly from brain tumor disease. It is considered a life-threatening, prominent, and serious disease. Thus, saving people-life requires automated detection and well-timed analysis of brain tumors. Accurate detection is the first and most important stage in diagnosing and treating brain tumors. Recently, the deep learning approach has been widely used in the medical sector, especially in diagnosing brain tumors. This paper introduces a novel low-complexity CNN model for detecting brain tumors using MRI images. A freely available dataset from Kaggle is employed. Initially, to select the baseline model, four famous CNN models, involving AlexNet, VGG-16, GoogLeNet, and ResNet-50, are examined and trained from scratch. The AlexNet model showed the best performance and got the highest accuracy of 98.8%. After improving it by inserting different layers, the proposed model reached an accuracy of 99.4%. To attain a low-complexity model, the final model has twenty-two layers, including nine convolutional blocks and four dropout layers. Other performance metrics include 99.67% precision, 99.02% recall, and 99.35% F1 score. © 2022 IEEE.

关键词： Tumors

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DuaNet: A Novel Lightweight CNN Model for Classifying Five-class Lung Diseases

DuaNet: A Novel Lightweight CNN Model for Classifying Five-c...

引用

2022 International Conference on Data Science and Intelligent Computing, ICDSIC 2022

作者： Al-Naqeeb, Duaa Al-Shamma, Omran Information Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq University of Information Technology and Communications Scientific Affairs Department Baghdad Iraq

ISBN: (纸本)9798350334883

Lung diseases rank among the world's top killers and disablers. Therefore, early identification is crucial for improving long-term survival rates and boosting the chances of recovery. Unlike the traditional method, machine learning (ML) showed great success in the medical field, mainly detecting and diagnosing different diseases. Most recently, the deep learning approach enhanced classification accuracy and eliminated the difficulty of manual feature extraction. As a literature conclusion, the model performance accuracy is inversely proportional to the number of lung diseases under consideration. In addition, no more than four classes (including normal) were considered previously. This work developed a lightweight CNN model, identified as DuaNet, with higher accuracy than the up-to-the-date models. The dataset has 930 X-ray images, categorized into five-class lung diseases: normal, tuberculosis, pneumonia COVID-19, pneumonia viral, and pneumonia bacterial. DuaNet comprises fifteen layers involving input, seven convolutional blocks, three max-pooling, three fully connected, and one output (Softmax) layer. Each convolutional block consists of a convolutional layer, Batch normalization, and ReLU activation function. The final model (DuaNet) obtained a performance accuracy of 99.87%, with 100% for other metrics. © 2022 IEEE.

关键词： Convolution

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Diabetic Retinopathy Detection and Classification Using Deep-wavelet and multiple classifiers

Diabetic Retinopathy Detection and Classification Using Deep...

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Information Technology (ACIT)

作者： Huda Dhari Nidhal K. El Abbadi Informatics Institute for Postgraduate Studies Iraqi Commission for Computers & Informatics Baghdad Iraq Al Mustaqbal Center for AI Applications Al Mustqbal University Babylon Iraq

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

The eyes, being the most intricate and indispensable sensory organs, serve as a window for gathering external images which are transformed to signals and delivered to the brain through the optic nerves. There are many diseases can affect the human eye. Diabetic Retinopathy (DR) is one of these eye diseases especially affecting people with diabetes. It caused a thickening of the outer retinal layer and is known to have different levels of severity. In recent times, automatic classification of certain ophthalmologic and cardiovascular diseases using analysis of retinal images has become commonplace within telemedicine settings. In this paper we proposed a novel methodology in two stages for the classification and detection of Diabetic Retinopathy (DR) along with various severities of the disease using deep wavelet as a tool for feature extraction (FE) techniques with modalities such as KNN, SVD and for detection and classification tasks. The dataset used is the Diabetic Retinopathy Grayscale Images dataset from Kaggle .In our case, we conclude that our approach is performing reasonably and provides a good results according to the comparative estimate.

关键词： Training Wavelet transforms Support vector machines Diabetic retinopathy Telemedicine Nearest neighbor methods Gray-scale Feature extraction Retina Overfitting

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Retinal Blood Vessels Segmentation Using Classical Edge Detection Filters and the Watershed

Retinal Blood Vessels Segmentation Using Classical Edge Dete...

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Information Technology (ACIT)

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Eye diseases are among the leading health risks people face today, with retinal blood vessel extraction being a crucial step in analyzing retinal images for medical diagnoses. The structures and geometric features of retinal blood vessels offer valuable insights into a patient's health and contribute to the diagnosis of conditions of eye diseases in addition to human body diseases such as diabetes and high blood pressure. The goal of all researchers is to prevent blindness by accurate diagnosis and timely treatment of these diseases. In this paper, we suggested an approach for extracting retinal blood vessels that consists of five stages. The first stage involves preprocessing, where the green channel of the input color image is selected, and edges are detected. In the second stage, we label all connected regions, close gaps, and fill holes. The third stage focuses on improving image contrast, applying a Gaussian filter, and using mathematical morphology to highlight the vessel network. The fourth stage involves segmenting the image into several regions using mathematical morphology and the watershed algorithm. Finally, in the fifth stage, small areas not belonging to blood vessels are removed based on their area and perimeter. Experimental results proved that the suggested approach produces a high-quality map of potential blood vessels. The performance metrics confirm the efficiency and accuracy of our approach.

关键词： Image segmentation Accuracy Image edge detection Morphology Watersheds Blood vessels Retina Feature extraction Eye diseases Biomedical imaging

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Empowering Optimizing Resource Management in 5G Telecommunication Networks: The Power of Machine Learning

Empowering Optimizing Resource Management in 5G Telecommunic...

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Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), ASU International Conference in

作者： Wasan Alaa Al-Hamami Mudhafar Jalil Jassim Ghrabat Mohammad Alaa Al-Hamami Iraqi Commission for Computers and Informatics the Informatics Institute for Postgraduate Studies Baghdad Iraq Management Information Systems Department Applied Science University Manama Kingdom of Bahrain

The emergence of 5G telecommunication technology has changed communication, promising high-speed data transfer, low latency, and extensive device connectivity. A key challenge lies in optimizing the performance and resource management of 5G networks, with supervised learning algorithms presenting a viable solution. These algorithms, supplemented by labeled training data, can accurately predict, and improve network behaviors, such as spectrum allocation, energy efficiency, quality of service (QoS) provisioning, and network optimization. This paper reviews these algorithms, delving into their applications and various strengths and limitations. It also discusses the challenges and future research directions in applying these algorithms to 5G networks, emphasizing the need to balance accuracy with complexity and ensuring fairness and security.

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Utilizing Blockchain Technologies with IoT: Review Paper 4

Utilizing Blockchain Technologies with IoT: Review Paper

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4th International iraqi Conference on Engineering Technology and Their Applications, IICETA 2021

作者： Khaki, Mays Adel Al-Mejibli, Intisar Shaheed Elameer, Amer S. Dept. Computer Science Iraqi Commission for Computers and Informatics Informatics Institute for Postgraduate Baghdad Iraq Baghdad Iraq

ISBN: (纸本)9781665494618

The core aim of the internet of things is providing communication capability between huge numbers of different entities "things". The Abundance feature of connected devices to the emergence of many security challenges for IoT data, which in turn stimulate researchers to search for appropriate solutions. The Blockchain is one of the security technologies which consider as a game changer in safeguarding the internet of things data. Through its impact, it has made the addition of more devices into the Blockchain ecosystem. Boosting the security of devices used in IoT to accelerate the rate of adoption of Blockchain technology. The main aim of this paper is to investigate the current literature to determine how the management of IoT has become independent and widespread doing away with the need of having to rely on one entity in the storage of data in the new technology, Blockchain assumes the role of a database making it different from the storage methods used in the past. In addition to, the authors have organized the utilization of Blockchain in a well-defined structure. Finally, this paper concluded that by utilization of Blockchain results in improved security and distribution using devices such as sensors which are connected efficiently with the aim of coming up with a secure database which gives rise to lots of business opportunities in the future. © 2021 IEEE.

关键词： Blockchain

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Enhanced Document Classification Using Ensemble Techniques

Enhanced Document Classification Using Ensemble Techniques

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International Conference on Developments in eSystems Engineering (DESE)

作者： Ahmed Hussein Salman Waleed Al-Jawher Iraqi Commission for Computers and Informatics / Informatics Institute of Postgraduate Studies Baghdad Iraq Department of Electronic and Communication Uruk University Baghdad Iraq

Both natural language processing and information retrieval rely on document classification. With the exponential growth of digital documents, there is an increasing demand for accurate and efficient document classification techniques. However, document classification faces several challenges, including processing vast amounts of data and extracting features from large volumes of unstructured data, which is complex. Moreover, numerous input features can be extracted, but not all of them are relevant to the classification problem. Single-classifier models often have limitations in capturing the complex patterns and nuances present in diverse document collections. In this paper, we propose a new approach to enhance document classification through the utilization of ensemble techniques. We combine multiple classifiers, including SVM, Naïve Bayes, and AdaBoost as base models, and aggregate their predictions with a neural network (NN) as a meta-model to improve overall classification performance. The suggested method aims to enhance accuracy, robustness, and generalization in classification by harnessing the diversity and complementary strengths of different classifiers. This research contributes to the field of document classification by utilizing the AdaBoost algorithm as a base model in stacking techniques applicable to document classification. The experimental results demonstrate that the proposed method outperforms the most recent methods in classifying documents in terms of accuracy, precision, and recall. The accuracy of the proposed system reached 87%, and the F1 score reached 87.8%.

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A New Multi-class Classification Method Based on Machine Learning to Document Classification.

A New Multi-class Classification Method Based on Machine Lea...

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International Conference on Developments in eSystems Engineering (DESE)

In the field of classification, input vectors are labeled with one of several predetermined classes. Automated systems scour a wide range of industries—from e-commerce to news outlets to blogs to directories to content curators—in search of applicable applications. To make suggestions about what to read and how to find it, natural language processing (NLP) uses document classification. Due to the difficulties that big datasets with many categories can provide for conventional document categorization approaches, this research uses machine learning for multi-class document classification. The suggested approach utilizes feature extraction and machine learning approaches to solve these issues. Features such as word frequency (TF) and term frequency-inverse document frequency (TF-IDF) are retrieved from documents to represent textual content, enabling the model to classify features based on word semantics. The method utilizes the chi-square test and the Bat Algorithm to choose features optimally. Random Forest (RF), Stochastic Gradient Descent (SGD), Adaptive Boosting (AdaBoost), and Cat Boost are only a few of the cutting-edge machine-learning algorithms used in the multi-class classification approach. These algorithms, which are trained to take advantage of the retrieved features, perform well when presented with multi-class document categorization challenges. Extensive testing on various datasets has validated the efficacy of the suggested methodology, demonstrating its improved accuracy and processing efficiency compared to traditional methods. Among the various available options, the Random Forest (RF) algorithm has proven to be the most effective, with an accuracy rate of 87.57%. This method’s flexibility makes it an invaluable tool in the NLP field since it may be successfully used in various document classification tasks.

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Comparison of Feature Selection Approaches for Breast Cancer Diagnosis using Supervised Machine Learning Algorithms 17

Comparison of Feature Selection Approaches for Breast Cancer...

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17th International Middle Eastern Simulation and Modelling Conference, MESM 2022

作者： Jassim, Mustafa Mohammed Al-Maliki, Manar Hassan Al-Azzawi, Waleed Khalid Jaber, Mustafa Musa Mohammed, Mohammed Qassim Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad10021 Iraq Iraqi Commission for Computers and Informatics Informatics Institute for Postgraduate Studies Baghdad10011 Iraq University of information Technology and communications Iraq

ISBN: (纸本)9789492859235

The high prevalence of breast cancer in women has increased considerably in recent years. Breast cancer is becoming the main cause of death among women worldwide. Automated feature extraction and classification algorithms can help physicians diagnose and identify breast cancer more quickly. Any advancement in cancer illness prediction and detection is critical to living a healthy life. As a result, high accuracy in cancer prognosis is critical for updating therapy aspects and patient survival standards. Machine learning technologies, which have been demonstrated to have a major influence on breast cancer prediction and early diagnosis, have become a hotspot for study and have been proven to be, a potent tool. In our research, we applied machine learning techniques. Following the gathering of data, the performance of the various approaches, is evaluated and compared. The study's major goal is to use ML algorithms to identify, diagnose breast cancer, as well as to establish which approach is the most successful in terms of the confusion matrix. © 2022 EUROSIS-ETI. All Rights Reserved.

关键词： Adaptive boosting

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