Iraqi Journal for Computers and Informatics
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Study of Factors Affecting the Production of Strategic Crops in Iraq Using Artificial Neural Networks
Get PDFDeveloped for financial and developmental planning, predictive models work on statistical techniques and artificial intelligence approaches. This project aims to evaluate and contrast Multiple Linear Regression MLR and Artificial Neural Networks ANN in terms of their predictive ability in Iraq\u27s wheat production estimation. The study makes use of wheat output data from 2007 to 2021. Evaluating Mean Absolute Percent Error MAPE alongside Mean Squared Error MSE and Mean Absolute Error MAE enabled two prediction accuracy measures to appraise the performance of both models. Artificial neural networks were found to outperform multiple linear regression since on agricultural data evaluations they produced more exact estimates with lower error levels. Until 2025, artificial neural networks provided superior tools for Iraqi agricultural planning and food security management and consequently became the chosen approach to forecast wheat yields.
Credit Fraud Recognition Based on Performance Evaluation of Deep Learning Algorithm
Get PDFOver time, the growth of credit cards and the financial data need credit models to support banks in making financial decisions. So, to avoid fraud in internet transactions which increased with the growth of technology it is crucial to develop an efficient fraud detection system. Deep Learning techniques are superior to other Machine Learning techniques in predicting the customer behavior of credit cards depending on the missed payments probability of customers. The BiLSTM model proposed to train on Taiwanese non-transactional dataset for bank credit cards to decrease the losses of banks. The Bidirectional LSTM reached 98% accuracy in fraud credit detection compared with other Machine Learning techniques
Data Collection and Preprocessing in Web Usage Mining: Implementation and Analysis
Get PDFData collection and data preprocessing are crucial stages in web usage mining, mainly because of the unstructured, diverse, and noisy nature of log data. During data collection, log file datasets are loaded and merged. Effective and comprehensive data preprocessing plays a vital role in ensuring the efficiency and scalability of algorithms used in the pattern discovery phase of web usage mining. This work aims to address these phases by introducing two innovative approaches. The first approach focuses on determining the device used for accessing the web, distinguishing between computers and mobile devices. The second approach aims to determine user sessions and complete paths by utilizing the referrer URL. The entire preprocessing pipeline has been implemented using the C# programming language, and the source code is available on GitHub at the following link: https://github.com/Mohammed91/Web-Usage-Mining
Conversational Health Bots for Telemedicine Services: Survey
Get PDFAn increasing number of individuals take refuge in telemedicine systems for medical diagnosis and treatment due to their numerous benefits, including reduced healthcare costs, enhanced efficiency, and the ability to treat and prevent a wide range of physical and mental health problems. To improve the health status and clinical findings of older and underserved individuals, healthcare institutions have expanded telemedicine services, integrating them with advanced assisted living systems and environments. Conversational chatbots, or dialogue systems, are software tools designed to emulate human interaction via the Internet. These conversational bots can engage in natural conversations and can be merged into websites, mobile apps, and messaging platforms.
Moreover, they can be used across various fields, such as healthcare, to support and enhance health services. An essential key feature of conversational chatbots is their ability to deliver swift and automated responses. In healthcare, these bots serve multiple purposes, including setting appointments, answering questions, and providing recommendations.
Modern-day conversational chatbots leverage artificial intelligence techniques, such as machine learning and natural language processing, to understand and respond to user inquiries effectively. This study will discuss the objectives of developing chatbot systems, the fundamental methodologies and datasets used, the primary challenges and limitations of existing works, and insights into future trends in chatbot development
An overview of skin cancer classification based on deep learning
Get PDFيعد سرطان الجلد الجلدي من أخطر الأمراض في العالم. التصنيف الصحيح للآفات الجلدية في خطوة أولية يمكن أن يساعد في خلق حكم سريري من خلال توفير الحكم الأمثل للمرض، مما قد يزيد من احتمالات العلاج في وقت مبكر من انتشار السرطان. وفي الوقت نفسه، يعد التصنيف التلقائي لسرطان الجلد أمرًا صعبًا بسبب عدم التوازن في معظم صور سرطان الجلد المستخدمة في التدريب. في الآونة الأخيرة، تم استخدام عدة طرق تعتمد على التعلم العميق على نطاق واسع في تصنيف سرطان الجلد لحل مشاكل التصنيف وتحقيق نتائج مقبولة. ومع ذلك، فإن المراجعات التي تحتوي على الصعوبات الحدية المذكورة أعلاه في تصنيف سرطان الجلد لا تزال نادرة. ونتيجة لذلك، تقدم هذه الورقة ملخصًا لأحدث إجراءات التعلم العميق لتصنيف سرطان الجلد. تبدأ هذه الورقة بمناقشة أنواع سرطانات الجلد وتليها مجموعة بيانات عامة متاحة لسرطان الجلد. وبعد ذلك، تم تسليط الضوء على بعض نماذج CNN المدربة مسبقًا والمستخدمة في التصنيف. أخيرًا، قمنا بتلخيص بعض فرص الإصابة بسرطان الجلد مثل اختلال توازن البيانات ومحدوديتها، وشبكة الخصومة التوليدية، ومجموعات البيانات المختلفة، وزيادة البيانات.Skin melanoma is one of the most dangerous diseases in the world. Correct classification of skin lesions in the first step can help create clinical judgment by providing an optimal judgment of the disease. As a result, the odds of treating the spread of cancer early may be increased. However, the automatic classification of skin cancer is tough because of the imbalance in most skin cancer images used in training. Several methods based on deep learning have been broadly used recently in skin cancer classification to resolve the problems in classification and attain acceptable outcomes. Nevertheless, reviews containing the aforementioned borderline difficulties in skin melanoma classification are still rare. Thus, this paper presents a summary of the newest deep learning procedures for classifying skin cancer. This paper starts with a discussion of skin cancer types, followed by the presentation of a public dataset available for skin cancer. Subsequently, some pretrained models of CNN used for classification are highlighted. Finally, some opportunities for skin cancer, such as data imbalance and limitation, generative adversarial network, various data sets, and data augmentation, are summarized
LUNG CANCER DETECTION IN LOW-RESOLUTION IMAGES
Get PDFOne of the most important prognostic factors for all lung cancer patients is the accurate detection of metastases. Pathologists, as we all know, examine the body and its tissues. On the existing clinical method, they have a tedious and manual task. Recent analysis has been inspired by these aspects. Deep Learning (DL) algorithms have been used to identify lung cancer. The developed cutting-edge technologies beat pathologists in terms of cancer identification and localization inside pathology images. These technologies, though, are not medically feasible because they need a massive amount of time or computing capabilities to perceive high-resolution images. Image processing techniques are primarily employed for lung cancer prediction and early identification and therapy to avoid lung cancer. This research aimed to assess lung cancer diagnosis by employing DL algorithms and low-resolution images. The goal would be to see if Machine Learning (ML) models might be created that generate higher confidence conclusions while consuming fractional resources by comparing low and high-resolution images. A DL pipeline has been built to a small enough size from compressing high-resolution images to be fed into an or before CNN (Convolutional Neural Network) for binary classification i.e. cancer or normal. Numerous enhancements have been done to increase overall performance, providing data augmentations, including augmenting training data and implementing tissue detection. Finally, the created low-resolution models are practically incapable of handling extremely low-resolution inputs i.e. 299 x 299 to 2048 x 2048 pixels. Considering the lack of classification ability, a substantial reduction in models’ predictable times is only a marginal benefit. Due to an obvious drawback with the methodology, this is disheartening but predicted finding: very low resolutions, essentially expanding out on a slide, preserve only data about macro-cellular structures, which is usually insufficient to diagnose cancer by itself
DDOS ATTACK DETECTION USING HYBRID (CCN AND LSTM) ML MODEL
Get PDFLSTM (Long Short-Term Memory) and CNN (Convolutional Neural Networks) are two types of deep learning algorithms; by combining the strengths of LSTM and CNN, researchers have developed deep learning models that can effectively detect SDN (Software-Defined Network) attacks including Distributed Denial of Service. These models effectively analyze network traffic, encompassing temporal and spatial characteristics, resulting in precise identification of malicious traffic.In this research, a hybrid model composed of CNN and LSTM is used to detect the DDoS attack in SDN network. Where the CNN component of the model can identify spatial patterns in network traffic, such as the characteristics of individual packets, while the LSTM component can capture temporal patterns in traffic over time, such as the timing and frequency of traffic bursts. The proposed model has been trained on a labeled network traffic dataset, with one class representing normal traffic and another class representing DDoS attack traffic. During the training process, the model adjusts its weights and biases to minimize the difference between its predicted output and the actual output for each input sample. Once trained, the hybrid model classifies incoming network traffic in the dataset as either normal or malicious with an initial accuracy of (78.18%) and losses of (39.77%) at the 1st epoch till it reaches an accuracy of (99.99%) with losses of (9.29×10-5) at the epoch number 500. It should be mentioned that the hybrid model of CNN and LSTM for DDoS detection is implemented using Python Anaconda platform with an ETA 28ms/step
COMPARATIVE STUDY OF CHAOTIC SYSTEM FOR ENCRYPTION
Get PDFChaotic systems leverage their inherent complexity and unpredictability to generate cryptographic keys, enhancing the security of encryption algorithms. This paper presents a comparative study of 13 chaotic keymaps. Several evaluation metrics, including keyspace size, dimensions, entropy, statistical properties, sensitivity to initial conditions, security level, practical implementation, and adaptability to cloud computing, are utilized to compare the keymaps. Keymaps such as Logistic, Lorenz, and Henon demonstrate robustness and high-security levels, offering large key space sizes and resistance to attacks. Their efficient implementation in a cloud computing environment further validates their suitability for real-world encryption scenarios. The context of the study focuses on the role of the key in encryption and provides a brief specification of each map to assess the effectiveness, security, and suitability of the popular chaotic keymaps for encryption applications. The study also discusses the security assessment of resistance to the popular cryptographic attacks: brute force, known plaintext, chosen plaintext, and side channel. The findings of this comparison reveal the Lorenz Map is the best for the cloud environment based on a specific scenario
A Survey on Cybercrime Using Social Media
Get PDFThere is growing interest in automating crime detection and prevention for large populations as a result of the increased usage of social media for victimization and criminal activities. This area is frequently researched due to its potential for enabling criminals to reach a large audience. While several studies have investigated specific crimes on social media, a comprehensive review paper that examines all types of social media crimes, their similarities, and detection methods is still lacking. The identification of similarities among crimes and detection methods can facilitate knowledge and data transfer across domains. The goal of this study is to collect a library of social media crimes and establish their connections using a crime taxonomy. The survey also identifies publicly accessible datasets and offers areas for additional study in this area
REVIEW ON DETECTION OF RICE PLANT LEAVES DISEASES USING DATA AUGMENTATION AND TRANSFER LEARNING TECHNIQUES
Get PDFThe most important cereal crop in the world is rice (Oryza sativa). Over half of the world\u27s population uses it as a staple food and energy source. Abiotic and biotic factors such as precipitation, soil fertility, temperature, pests, bacteria, and viruses, among others, impact the yield production and quality of rice grain. Farmers spend a lot of time and money managing diseases, and they do so using a bankrupt "eye" method that leads to unsanitary farming practices. The development of agricultural technology is greatly conducive to the automatic detection of pathogenic organisms in the leaves of rice plants. Several deep learning algorithms are discussed, and processors for computer vision problems such as image classification, object segmentation, and image analysis are discussed. The paper showed many methods for detecting, characterizing, estimating, and using diseases in a range of crops. The methods of increasing the number of images in the data set were shown. Two methods were presented, the first is traditional reinforcement methods, and the second is generative adversarial networks. And many of the advantages have been demonstrated in the research paper for the work that has been done in the field of deep learning