Jaber Karimpour

Sequential object-oriented software remains difficult to parallelize because several important decisions are not entirely local. A useful decomposition should consider architectural structure, execution frequency, communication latency, and how much communication can overlap with local computation. In contrast, many traditional modularization methods focus on cohesion, coupling, or communication cost without explicitly considering whether an unavoidable remote call can be issued early enough for part of its latency to be hidden by independent work. DTMC-GA addresses this problem by combining a Discrete-Time Markov Chain (DTMC) execution model, a Distributive Execution Time (DET) estimator, and a Genetic Algorithm (GA) search procedure. Guarded call-use displacement enlarges modeled overlap windows only when the relevant dependence, ordering, and safety conditions are satisfied. The public artifact contains five local benchmark fixtures, five comparison methods, 30 retained runs per algorithm–fixture pair, raw outputs, statistical scripts, regenerated figures, Docker support, continuous-integration verification, and a Zenodo DOI. Within this released five-method panel, DTMC-GA achieved the highest aggregate mean modeled speedup of 2.708 ± 0.329. A more recent NSGA-III comparator was also evaluated using matched fixtures, DTMC–DET inputs, constraints, random seeds, population size, and generation budget. NSGA-III achieved an aggregate mean modeled speedup of 2.737 ± 0.323, 1.07% above DTMC-GA, although none of the five fixture-level paired differences remained significant after Bonferroni correction. A separate sparse-graph scalability study covered 50, 100, 250, 500, 750, and 1000 classes; mean analysis time increased from about 0.024 to 0.286 s, while peak memory rose from about 119.1 to 120.9 MiB. The study also includes an executed OpenCV 4.7.0 calibration using a fixed five-convolution DNN workload. Across 30 matched pairs, native-parallel OpenCV achieved a measured ratio-of-means speedup of 1.8691×. The bounded empirical DTMC-GA/DET rerun over 30 seeds produced a mean modeled speedup of 1.0000× with an SD of 0.0000, giving an absolute modeled-versus-measured difference of 46.50%. Because the measured parallel comparator is OpenCV’s native threaded implementation rather than a DTMC-GA-generated source rewrite, this experiment is interpreted as real-runtime model calibration rather than direct measured DTMC-GA optimized speedup.

Accurate classification and efficient retrieval of histopathological images are essential for the diagnosis of lung adenocarcinoma (LUAD). Existing deep learning approaches for Content-Based Histopathological Image Retrieval (CBHIR) typically generate single-scale embeddings, missing the richer spatial context from earlier network stages. We propose a unified framework composed of: (1) a ConvNeXt V2 backbone with an integrated Convolutional Block Attention Module (CBAM) for multi-scale feature extraction, (2) an Adaptive Weighted Fusion Neck with learnable softmax-normalized weights, and (3) a novel Hybrid Representation Head producing an 18,496-dimensional descriptor by concatenating global, spatial, and attention-weighted features. Evaluated on the WSSS4LUAD dataset (10,087 patches, four tissue classes), our model achieves 85.03% accuracy (5-fold CV: 83.35 ± 0.93%), F1-score of 0.8116, mean Average Precision (MAP) of 0.8323 for retrieval, and an Expected Calibration Error (ECE) of 0.0378. Ablation experiments confirm that all proposed modules contribute positively, with the Attention Branch being the most impactful (Δ = −2.13%). The framework further provides Gradient-weighted Class Activation Mapping (Grad-CAM) explainability for clinical interpretability.

Here, we propose a composite system that uses text summarization (TS) and question answering (QA) to supplement the IR process of long documents. Most previous studies have used separate approaches, i.e., either TS or QA. The aim of this paper is to develop an interaction between TS and QA in three stages to enhance IR performance. First, SBERT is used for summarization. Second, an RAG method is employed to retrieve information and generate answers. In the architecture of RAG, retrieval of the document is fulfilled via all-MiniLM-L6-v2, while answer generation is performed via the T5 and BART-large-cnn models. Third, the retrieved answers are assessed and compared with a baseline system in which the documents are treated without summarization. The proposed system aims to improve the quality of retrieved information and accuracy of answers generated by TSQA in a unified pipeline. Experimental evaluation conducted on the NIPS dataset demonstrates that the proposed approach significantly enhances summary informativeness and answer accuracy compared with traditional single-task approaches. The simulation results show improvements of 20.83% in text similarity and 2.38% in BERT scores for answer generation compared with the standard RAG baseline without summarization.

Resource-limited settings continue to face challenges in the identification of COVID-19, bacterial pneumonia, viral pneumonia, and normal lung conditions because of the overlap of CT appearance and inter-observer variability. We justify a hybrid architecture of deep learning which combines hand-designed descriptors (Histogram of Oriented Gradients, Local Binary Patterns) and a 20-layer Convolutional Neural Network with dual self-attention. Handcrafted features were then trained with Support Vector Machines, and ensemble averaging was used to integrate the results with the CNN. The confidence level of 0.7 was used to mark suspicious cases to be reviewed manually. On a balanced dataset of 14,000 chest CT scans (3500 per class), the model was trained and cross-validated five-fold on a patient-wise basis. It had 97.43% test accuracy and a macro F1-score of 0.97, which was statistically significant compared to standalone CNN (92.0%), ResNet-50 (90.0%), multiscale CNN (94.5%), and ensemble CNN (96.0%). A further 2–3% enhancement was added by the self-attention module that targets the diagnostically salient lung regions. The predictions that were below the confidence limit amounted to only 5 percent, which indicated reliability and clinical usefulness. The framework provides an interpretable and scalable method of diagnosing multiclass lung disease, especially applicable to be deployed in healthcare settings with limited resources. The further development of the work will involve the multi-center validation, optimization of the model, and greater interpretability to be used in the real world.

In the field of blockchain technology, ensuring secure and efficient access control for smart contracts remains a critical challenge. Traditional methods are often complex and resource-intensive, potentially hindering widespread adoption. This study proposes a novel machine learning-based approach to enhance access control mechanisms. Specifically, we classify users as either benign or potentially malicious based on transaction behavior and interaction patterns. A Support Vector Machine (SVM) classifier, combined with a Genetic Algorithm (GA) for dimensionality reduction, is applied to a dataset containing 50,000 transaction records from 1,000 blockchain addresses. The model achieved an accuracy of 94% on the test set and effectively distinguished users based on server interaction frequency and connection duration. Through visual analysis and comprehensive evaluation, we demonstrate that the proposed method improves both anomaly detection and operational efficiency. This approach has the potential to bolster trust and facilitate broader adoption of blockchain-based applications.

As the volume of data and data-generating equipment in healthcare settings grows, so do issues like latency and inefficient processing inside health monitoring systems. The Internet of Things (IoT) has been used to create a wide variety of health monitoring systems. Most modern health monitoring solutions are based on cloud computing. However, large-scale deployment of latency-sensitive healthcare applications is hampered by the cloud’s design, which introduces significant delays during the processing of vast data volumes. By strategically positioning servers close to end users, fog computing mitigates latency issues and dramatically improves scaling on demand, resource accessibility, and security. In this work, we describe a new load-balancing strategy based on the Strength Pareto Evolutionary Algorithm 2 (SPEA2) for distributing work among fog nodes in a large-scale health monitoring system. We ran comprehensive simulations in the iFogSim toolkit to verify the efficacy of the proposed method, comparing the results to the cloud-only implementation, the Fog Node Placement Algorithm (FNPA), the Load Balancing (LAB) scheme, and the Load Balancing Scheme (LBS) in terms of latency and network utilization. The proposed health monitoring system deployment drastically decreases latency and network use in comparison to cloud-only, FNPA, LAB, and LBS schemes.

In recent years, implementing reinforcement learning in autonomous mobile robots (AMRs) has become challenging. Traditional methods face complex trials, long convergence times, and high computational requirements. This paper introduces an innovative strategy using a customized spiking neural network (SNN) for autonomous learning and control of mobile robots (AMR) in unknown environments. The model combines spike-timing-dependent plasticity (STDP) with dopamine modulation for learning. It utilizes the Izhikevich neuron model, leading to biologically inspired and computationally efficient control systems that adapt to changing environments. The performance of the model is evaluated in a simulated environment, replicating real-world scenarios with obstacles. In the initial training phase, the model faces significant challenges. Integrating brain-inspired learning, dopamine, and the Izhikevich neuron model adds complexity. The model achieves an accuracy rate of 33% in reaching its target during this phase. Collisions with obstacles occur 67% of the time, indicating the struggle of the model to adapt to complex obstacles. However, the model’s performance improves as the study progresses to the testing phase after the robot has learned. Its accuracy surges to 94% when reaching the target, and collisions with obstacles reduce it to 6%. This shift demonstrates the adaptability and problem-solving capabilities of the model in the simulated environment, making it more competent for real-world applications.

Breast cancer is one of the most prevalent cancers among women worldwide, and early detection of the disease can be lifesaving. Detecting breast cancer early allows for treatment to begin faster, increasing the chances of a successful outcome. Machine learning helps in the early detection of breast cancer even in places where there is no access to a specialist doctor. The rapid advancement of machine learning, and particularly deep learning, leads to an increase in the medical imaging community’s interest in applying these techniques to improve the accuracy of cancer screening. Most of the data related to diseases is scarce. On the other hand, deep-learning models need much data to learn well. For this reason, the existing deep-learning models on medical images cannot work as well as other images. To overcome this limitation and improve breast cancer classification detection, inspired by two state-of-the-art deep networks, GoogLeNet and residual block, and developing several new features, this paper proposes a new deep model to classify breast cancer. Utilizing adopted granular computing, shortcut connection, two learnable activation functions instead of traditional activation functions, and an attention mechanism is expected to improve the accuracy of diagnosis and consequently decrease the load on doctors. Granular computing can improve diagnosis accuracy by capturing more detailed and fine-grained information about cancer images. The proposed model’s superiority is demonstrated by comparing it to several state-of-the-art deep models and existing works using two case studies. The proposed model achieved an accuracy of 93% and 95% on ultrasound images and breast histopathology images, respectively.