Hedieh Sajedi

Web robots account for over half of all web traffic and typically monitor resources uniformly, unlike humans, who tend to focus on specific topics. This research examines the impact of a machine learning algorithm, known as Extreme Gradient Boosting (XGBoost), on identifying automated robots. XGBoost is notable for its strengths in parallel processing and regularization. The study analyzes and classifies data from 4,091,155 requests, with a primary dataset of 67,352 sessions divided into universal and dependent files. The universal data file contains 30 distinct features, while the dependent file has 5. Alongside XGBoost, two additional algorithms—Random Forest and Hist Gradient Boosting—were utilized to enhance the study’s validity. The three models achieved a comparable accuracy to one another across the datasets for five-fold cross-validation, achieving accuracy figures of 0.8407–0.8413 on the dependent dataset and 0.9671–0.9771 on the universal dataset. Paired tests for significance indicated that the difference in performance of XGBoost to both Random Forest and HistGB was significant on the universal data but not the dependent data ( p < 0.01 on both, vs. p > 0.29 on both respectively). The algorithms also differed somewhat in computational cost, where HistGB took a maximum of 3.1x the training time compared to XGBoost, but then recorded 0.1x the inference cost of both compared to the other models suggesting a trade-off between training time and prediction time that may be important when choosing models for real world implementation.

Accurate brain tumor classification from neuroimaging modalities such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) is critical for clinical diagnosis. However, fine-tuning large deep learning models on moderately sized medical datasets often demands extensive computational resources. This work demonstrates that strategic transfer learning can address this challenge. We propose a dual-branch architecture that combines two pre-trained backbones: a ConvNeXt to capture local patterns and a Swin Transformer to model global contextual dependencies. A cross-attention mechanism fuses these complementary features, while a differential learning rate strategy accelerates convergence. Extensive evaluation on eight publicly available brain MRI and CT datasets spanning binary and multi-class classification tasks shows that our model outperforms state-of-the-art baselines on most datasets and metrics and matches the best-performing models in remaining cases, all with substantially fewer training epochs. The main contribution of this work is the introduction of a dual-branch fusion architecture coupled with a differential learning rate strategy, enabling efficient adaptation of pre-trained backbones for medical image analysis.

Cancer occurs when healthy cells in the body grow abnormally and out of control. Leukemia is a type of cancer that affects White Blood Cells (WBCs) and can cause a lethal infection and early death. Identification and classification of different types of leukemia are performed manually and automatically. The doctors analyze blood samples under a microscope and consider any changes in the number and structure of WBCs as a sign of cancer in the manual method. It is a time-consuming, inaccuracy-prone process that depends on the expertise and skill of the physician and the type of laboratory equipment. In recent years, more automated methods of identifying and classifying leukemia have been developed with the help of Artificial Intelligence (AI) and Computer Vision (CV), with the aim of overcoming the challenges of manual approaches. This paper introduces two types of attention blocks, Parallel Cognitive Attention Block (PCAB) and Sequential Cognitive Attention Block (SCAB), to integrate into the architecture of any Convolutional Neural Network (CNN). Each of the proposed attention blocks is composed of the channel and spatial attention sub-blocks. They extract the structure and location of WBCs in the feature maps, similar to the ventral and dorsal streams in the human brain. The PCAB and SCAB were embedded in the architecture of the ResNet18 and MobileNetv4. The baseline and attention-based networks are trained, validated, and tested by two types of data splitting on the four leukemia datasets, including ALL, ALL-IDB2, C-NMC, and Mixture-Leukemi (ALL-IDB2+Munich AML Morphology), with the same experimental conditions for 30 epochs. The classification results demonstrate that the proposed model (MobileNetv4PCAB) achieved better performance metrics than others on all datasets in the test steps. It showed that the suggested model achieved the accuracy values of 100%, 100%, 93.61%, and 99.4%, and the F1-score values of 100%, 100%, 95.64%, and 99.3% with ALL, ALL-IDB2, C-NMC, and Mixture-Leukemia datasets, respectively. We confirmed that the proposed model outperforms existing state-of-the-art methods.

Dysgraphia, a neurodevelopmental disorder impacting writing abilities, is often overlooked or misdiagnosed, affecting academic and daily life activities. It affects both children, especially school-aged, and adults, potentially stemming from brain trauma or neurological diseases. Early diagnosis is crucial for effective management and intervention. With advancements in artificial intelligence (AI), there is potential to improve early diagnosis and intervention through automated detection methods. This scoping review aims to explore available studies employing AI-based models for dysgraphia diagnosis and identify the highest performance models and their challenges to suggest future improvements. Comprehensive searches were conducted across PubMed, Scopus, Web of Science, IEEE Xplore, and SpringerLink until April 2024. Included studies are original research articles focusing on dysgraphia detection using AI-based predictive models with image processing techniques on handwritten images. Data extraction was done using a structured form in Google Sheets, capturing study characteristics, datasets, methods, and results. Out of 177 initial papers, 21 met the inclusion criteria. Studies span nearly a decade, with 38% being conference papers and 62% journal articles from various countries. AI models, particularly Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), demonstrated high accuracy rates, often surpassing 90%, with comprehensive feature extraction methods enhancing performance. Significant challenges include small sample sizes and language-specific models. AI-based models, especially advanced ones like CNNs and SVMs, significantly enhance dysgraphia diagnosis, offering faster and more accurate assessments than traditional methods. Future research should focus on larger, more diverse datasets, language-independent models, and standardized evaluation metrics. Integrating AI-based tools in educational and healthcare settings can revolutionize dysgraphia management, improving academic outcomes and support for affected individuals.

Anomaly detection or outlier is one of the challenging subjects in unsupervised learning . This paper is introduced a student-teacher framework for anomaly detection that its teacher network is enhanced for achieving high-performance metrics . For this purpose , we first pre-train the ResNet-18 network on the ImageNet and then fine-tune it on the MVTech-AD dataset . Experiment results on the image-level and pixel-level demonstrate that this idea has achieved better metrics than the previous methods . Our model , Enhanced Teacher for Student-Teacher Feature Pyramid (ET-STPM), achieved 0.971 mean accuracy on the image-level and 0.977 mean accuracy on the pixel-level for anomaly detection.

The Internet of Things (IoT) is a concept by which objects find identity and can communicate with each other in a network. One of the applications of the IoT is in the field of medicine, which is called the Internet of Medical Things (IoMT). Acute Lymphocytic Leukemia (ALL) is a type of cancer categorized as a hematic disease. It usually begins in the bone marrow due to the overproduction of immature White Blood Cells (WBCs or leukocytes). Since it has a high rate of spread to other body organs, it is a fatal disease if not diagnosed and treated early. Therefore, for identifying cancerous (ALL) cells in medical diagnostic laboratories, blood, as well as bone marrow smears, are taken by pathologists. However, manual examinations face limitations due to human error risk and time-consuming procedures. So, to tackle the mentioned issues, methods based on Artificial Intelligence (AI), capable of identifying cancer from non-cancer tissue, seem vital. Deep Neural Networks (DNNs) are the most efficient machine learning (ML) methods. These techniques employ multiple layers to extract higher-level features from the raw input. In this paper, a Convolutional Neural Network (CNN) is applied along with a new type of classifier, Higher Order Singular Value Decomposition (HOSVD), to categorize ALL and normal (healthy) cells from microscopic blood images. We employed the model on IoMT structure to identify leukemia quickly and safely. With the help of this new leukemia classification framework, patients and clinicians can have real-time communication. The model was implemented on the Acute Lymphoblastic Leukemia Image Database (ALL-IDB2) and achieved an average accuracy of %98.88 in the test step.