Journal of Innovations in Computer Science and Engineering (JICSE)

Abstract Skin cancer is one of the deadliest but most prevalent types of cancer; as such, early diagnosis is urgently required to improve patient outcomes. This work presents a collaborative deep learning model that classifies skin cancer with respect to three different networks: EfficientnetB1, EfficientnetB2, and EfficientnetV2s on dermoscopic images. The proposed collaborative model has a multi-head attention mechanism, ensuring that this model has a better attention capability for improving its accuracy in the task of classification. The HAM10k dataset provided the proposed model with a platform for fine tuning with transfer learning, along with some augmentation techniques to handle class imbalance challenges and feature variations of lesions. The results for the ensemble model combined with Multi-Head Attention were very high: an accuracy of 97.11%, and precision, recall, and F1-score are also very high. These findings prove that our approach can dramatically improve automation in skin cancer detection. Therefore, it will be helpful in clinical dermatology for early diagnosis in medicine.

Abstract Abstract— Detecting white blood cells (WBC) in microscopic images is essential in medical diagnosis. Manual analysis of these images is time-consuming and has a high error rate. Using object detection for WBCs detection with deep convolutional neural networks (CNN) can be considered a practical and effective solution. In this study, a CNN model is proposed to classify these images. In order to achieve optimal training performance, CNNs have many hyperparameters, such as dropout rate, number of hidden units in each hidden layer, activation function, loss function and optimizer, which need to be optimized. Therefore, a hyperparameter optimization approach based on a genetic algorithm is suggested, which can then be used to select the best combination parameters to improve accuracy and efficiency in detecting white blood cells in microscopic images. This new approach is significant and flexible for medical technicians to use in clinical practice for examining blood cell microscopy. In this research, the images were classified into five classes and the mean accuracy of the model for the five classes was 87%, which is considered a good accuracy for classification into five classes.

Abstract Understanding dynamic systems like disease outbreaks, social influence, and information diffusion requires effective modeling of complex networks. Traditional evaluation methods for static networks often fall short when applied to temporal networks. This paper introduces DGSP-GCN (Dynamic Graph Similarity Prediction based on Graph Convolutional Network), a deep learning-based framework that integrates graph convolutional networks with dynamic graph signal processing techniques to provide a unified solution for evaluating generative models and detecting anomalies in dynamic networks. DGSP-GCN assesses how well a generated network snapshot matches the expected temporal evolution, incorporating an attention mechanism to improve embedding quality and capture dynamic structural changes. The approach was tested on five real-world datasets: WikiMath, Chickenpox, PedalMe, MontevideoBus, and MetraLa. Results show that DGSP-GCN outperforms baseline methods, such as time series regression and random similarity assignment, achieving the lowest error rates (MSE of 0.0645, MAE of 0.1781, RMSE of 0.2507). These findings highlight DGSP-GCN's effectiveness in evaluating and detecting anomalies in dynamic networks, offering valuable insights for network evolution and anomaly detection research.

Abstract 3D object detection for self-driving cars has emerged as a critical challenge, largely due to the constraints of computing resources and the requirement for real-time processing. For evaluation, we utilize the KITTI benchmark dataset, which is widely used for self-driving cars and 3D object detection research. In this paper, an adaptive Weighted knowledge distillation approach is proposed to enhance detection accuracy while improving computational efficiency. Based on the performance of the teacher model, point clouds are divided into TPS (Teacher Performs Strongly) and TPW (Teacher Performs Weakly). The student model adapts its learning strategy dynamically: for TPS point clouds, it closely imitates the teacher by increasing the distillation weight, whereas for TPW point clouds, it prioritizes learning from raw data, reducing reliance on the teacher’s guidance. Additionally, a data pruning mechanism creates a smaller dataset from the KITTI benchmark based on the teacher model’s performance, while maintaining the TPS-TPW ratio. Experimental results indicate that the student model achieves comparable, and in some cases superior, performance to the teacher model. Specifically, it enhances recall by up to 1.5% and precision by up to 2.2% in complex scenarios. The student model is a lightweight network that learns from the teacher through knowledge distillation [28] and is solely used during execution. This design reduces execution time by nearly 50% while maintaining high detection accuracy. These findings emphasize the effectiveness of the proposed framework in real-time 3D object detection, making it well-suited for deployment in resource-constrained environments such as self-driving cars.

Abstract With the rapid expansion of the Internet and social networks, the volume of images available online has increased dramatically, making it challenging for users to find relevant content. To address this problem, we propose incorporating emotion analysis as a key factor in understanding user preferences, thereby creating a more personalized and effective image recommendation system. In this article, we examine two approaches to utilizing emotional features in image recommendation. The first approach integrates emotional features directly into the feature vector used for training the recommendation model. The second approach refines recommendations through emotion-based postprocessing, where emotional proximity between users and images is used to re-rank recommendations. This study emphasizes the value of emotion analysis in advancing the personalization and efficacy of social image recommendation systems. Experimental results indicate that both approaches significantly improve recommendation performance, achieving higher metrics such as Recall@k and Precision@k. These findings demonstrate that emotional analysis enhances personalization and effectiveness in social image recommendation systems.

Abstract Graph-structured data, common in real-world applications, captures entities (nodes) and their relationships (edges). While traditional methods integrate node content and neighborhood information to represent nodes in a latent space, random walks—despite being grounded in graph topology—suffer from limitations such as bias towards high-degree nodes, slow convergence, and difficulty in handling disconnected components. To address these issues, we introduce the "Feature-Based Random Walk on Graphs" (FARW), an advanced method that prioritizes node similarity in random walks. Unlike traditional approaches, FARW determines movement based on node features, enabling a more comprehensive analysis of complex networks. This feature-based approach improves the representation of heterogeneous graphs and enhances performance on a variety of tasks. Moreover, FARW demonstrates greater robustness when the graph structure changes. Experiments on three datasets—Cora, PubMed, and CiteSeer—show that FARW outperforms traditional structure-based random walks and the Node2Vec method, achieving accuracies of 87%, 83%, and 65%, respectively. These results suggest that incorporating node features during random walks improves the efficiency and accuracy of network analysis across diverse applications

Abstract The concept of six degrees of separation stands as a significant phenomenon, positing that any two independent entities worldwide can connect through a chain of no more than six acquaintances. This article delves into the study of this phenomenon across various network models, aiming to quantify the rates of information propagation, idea dissemination, disease transmission, and predictive trends in society and economics. We extend the examination beyond the conventional notion of "six degrees of separation" by investigating the factors impacting degrees of separation and Milgram's condition in complex networks. Our objective is to elucidate that the actual degree of separation within a network is intricately tied to its structure and various parameters. Instead of being a universal rule, this concept can be construed as a condition that networks must satisfy. We explore Milgram's condition in diverse network models, encompassing random, small-world, and scale-free networks, while scrutinizing the impact of the frequency and length of cycles on degrees of separation. We introduce a novel criterion, termed multiplicity within the network and assess its relationship with the Hamming distance. We evaluate the effectiveness of Milgram's condition and degrees of separation in the context of these two parameters. Our findings underscore the close association between Milgram's condition and degrees of separation with the specific network model and its structure.

Abstract The de Bruijn graph, initially proposed as a topological architecture for interconnection networks, offers unique attributes. These graphs are regular, Eulerian, and Hamiltonian, boasting a small diameter close to optimal connectivity. Their low average distance, small diameter, and high connectivity contribute to remarkable fault tolerance against both node and edge failures. Comprehensively, these graphs serve as pivotal components in word-representing networks, finding applications in various scientific and engineering domains, particularly in genome assembly. They play a significant role in bioinformatics, information theory, coding, communication networks, and multiprocessors. Additionally, de Bruijn graphs are utilized in peer-to-peer (P2P) networks and distributed hash tables (DHT), demonstrating their versatility. Moreover, de Bruijn graphs can serve as a robust infrastructure for modeling online/offline user behavior. In this article, we delve into the different types of de Bruijn graphs and their unique properties from a graph theory perspective. Our focus is on evaluating the reliability of these graphs concerning resilience, fragility, and vulnerability to random failures and targeted attacks on both nodes and edges.

Abstract Tokenization is a critical stage in text preprocessing and presents numerous challenges in languages like Persian, where there is no deterministic word boundary. These challenges include the identification of multi-function morphemes, separation of punctuation marks, omission of spaces between tokens, and handling extra spaces inside words.



Typically, the evaluation of tokenizers focuses on overall performance, and test data does not necessarily cover all challenging linguistic phenomena. As a result, strengths and weaknesses of tokenizers in addressing specific challenges are not independently assessed. This paper examines the challenges posed by the Persian script in detecting word boundaries and evaluates the performance of seven tokenizers in handling these issues. A test set of 4091 tokens across 483 sentences was prepared, with 1010 considered as challenging tokens. The tokenizers were evaluated using this dataset.



The results indicate varying performance among tokenizers when dealing with Persian orthography. Some tokenizers performed better in separating compound words, while others excelled in identifying and preserving zero-length joiners (half-space). A detailed comparison reveals that no tokenizer fully addresses all challenges, highlighting the need for improved algorithms and more sophisticated solutions for Persian word boundary detection.



By introducing a comprehensive benchmark and identifying the strengths and weaknesses of available tokenizers, this study paves the way for the development of better Persian language processing tools.

Abstract Moore and Shannon introduced a probabilistic model in which network nodes are assumed to be completely reliable, and communication links or edges can fail with a given probability, such as p. The main problem is determining the probability that the network will remain connected under these conditions, meaning establishing a route between the source and destination terminals. If all links are operational with the same probability of p, the reliability of the entire network is described as a function of p, leading to the reliability polynomial of the network. Moore and Shannon proposed their reliability analysis on specific networks known as hammock networks. Such networks can be well adapted to array-based circuits such as FinFET, VSFET, MOSFET, NEMS, and CNFETs. In this article, focusing on hammock networks, we utilize their combination to design and implement MOS-based transistors, i.e., nMOS and pMOS, and implement basic logical gates based on such networks. To determine the reliability polynomial coefficients, various methods have been presented, most of which exhibit computational complexity due to the recursive property. In practice, for circuits with large orders and sizes, the exact calculation of reliability polynomial coefficients falls into the NP-hard complexity class. In this study, while reviewing the existing problems, efficient methods have been employed to determine the polynomial coefficients of reliability. To ensure fair and accurate comparisons and evaluations, simulation results are utilized to extract performance and reliability measures for all circuits. The reliability of the investigated networks is then compared and analyzed.

Abstract Process mining is a new research area that bridges the gap between data science and process science. Among the subfields of process mining, predictive process monitoring aims to predict process features such as the next event, outcome, and remaining time. In recent years, increasing research has been conducted in the area of remaining time prediction.
In this paper, we present an interpretable method for remaining time prediction. Interpretability is an advantageous feature for a prediction method, because it provides the necessary advice and warnings to the organization’s experts during the process execution when it is used in a recommender system. In the proposed method, we utilize distance metric learning methods to develop a distance function for process events. The distance function can be used to find the most similar cases to the intended case, and then the remaining time of the similar cases is used as the indicator of the remaining time of the intended case. Our proposed method has been evaluated on three datasets, and the evaluations revealed that it significantly outperforms the state-of-the-art baseline in two datasets while achieving comparable accuracy in the third dataset. Additionally, our proposed method provides interpretation, while the competitor methods are not interpretable.

Abstract The stock market plays an imperative role in the entire financial market. The intricate and multifaceted nature of the stock market poses a challenge for investors seeking to establish a reliable and profitable trading approach. This paper aims to address this issue by leveraging two methodologies based on Deep Reinforcement Learning (DRL), namely Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG), incorporating Convolutional Neural Network (CNN) and Gate Recurrent Unit (GRU) architectures, along with an attention mechanism to boost the decision making based on time-series stock data. This adaptation enables the model to focus on essential features and time periods within the stock data, leading to more successful and higher-quality trading choices. Following extensive experimentation and analysis, our proposed RLbased trading demonstrates improved accuracy and profitability compared to similar approaches. The proposed methodology strives to offer investors a dependable and lucrative trading strategy, ultimately leading to a more prosperous and efficient stock trading experience.