Journal of Innovations in Computer Science and Engineering (JICSE)

Abstract I. Abstract— This paper examines the legal frameworks governing the criminal liability of artificial intelligence (AI) in Iran and Saudi Arabia, focusing on how both countries address the evolving role of AI in criminal acts. With the rapid advancement of AI technologies—from narrow (weak) AI, which performs specific tasks, to general (strong) AI, capable of autonomous decision-making—complex legal and ethical questions have emerged. Specifically, this paper examines the applicability of three theoretical models of AI criminal liability: Perpetration-By-Another Liability, Natural-Probable-Consequence Liability, and Direct Liability.

II. The comparative analysis highlights that despite differences in legal traditions and societal contexts, both Iran and Saudi Arabia recognize that AI itself cannot bear criminal responsibility, and instead, liability is attributed to human actors, such as developers, users, and operators. The findings suggest that integrating technological progress with ethical and legal safeguards, grounded in Islamic jurisprudence, is essential for addressing the challenges posed by AI-related crimes in both jurisdictions.

Abstract Abstract—In today's world, DDoS attacks are becoming more common and complex; thus, they constitute a great challenge for network security under the auspices of SDN. The research effort described here proposes an integrated hybrid model called "LDA-ML," which leverages some state-of-the-art machine learning methods: LDA, naive bayes, random forest, and logistic regression. We optimize the data analysis process by leveraging LDA for feature selection and dimensionality reduction, followed by a sequential application of the classifiers to exploit their strengths. Evaluated on the CICDDoS-2019 dataset, the proposed model has achieved an outstanding accuracy of 98.98%, indicating the efficacy of the model in correctly classifying benign versus attack traffic. All of the above underlines the robustness of the proposed LDA-ML model, pointing to great potential for its application to continuously improve cybersecurity strategies against DDoS threats in SDN architectures. This holistic approach offers improvements in detection, while it also enriches diagnostic insights-an important contribution to finding effective security solutions in increasingly dynamic network environments.

Abstract Abstract— The issue of intrusion in security presents a fundamental challenge that can lead to serious damage in IT systems. Intrusion Detection Systems (IDS) serve as effective tools for identifying intrusion activities and generating alerts. However, traditional IDS methods often face issues such as low accuracy and long training times. Therefore, enhancing the performance and efficiency of these systems is crucial. The proposed approach in this study leverages evolutionary optimization algorithms combined with machine learning approaches to improve accuracy and training speed in IDS and better manage large volumes of data. This combination leads to the development of an Evolutionary Neural Network (ENN) that enhances and optimizes IDS performance. In this approach, BUZOA and Ant Colony Optimization (ACO) algorithms are used for feature selection, and decision tree, k-nearest neighbor, support vector machine, and deep neural network algorithms are used for classification and intrusion detection. The dataset used in this research is from the CICDDOS2019 database, containing 54,000 samples and 22 initial features. The experimental results indicate that among the metaheuristic algorithms BUZOA and ACO, and their combinations with decision tree, k-nearest neighbor, and support vector machine, the BUZOA-CNN hybrid algorithm with an average RMSE of 0.0117 and an accuracy of 96.32% performs better than other algorithms.

Abstract Abstract—The rapid advancement of artificial intelligence (AI) offers new avenues for enhancing the identification, control, and autonomous operation of marine vehicles. This study investigates the application of AI techniques in maritime environments, focusing on object detection, navigation, and autonomous control to support safe and efficient operations in various marine conditions. Key objectives include evaluating machine learning models for identifying and tracking marine vehicles and the development of intelligent control algorithms that can adapt to dynamic oceanic settings. Methods involved training convolutional neural networks (CNNs) on datasets of marine images for object identification and using reinforcement learning (RL) algorithms to optimize the control systems of autonomous marine vehicles. Results demonstrate that CNN-based models achieve high accuracy in vehicle identification, even under challenging visual conditions such as low lighting or occlusion. At the same time, RL-driven control systems adapt effectively to complex, fluctuating marine environments. Simulated and real-world testing indicated that these AI techniques improve vessel maneuverability and response times, leading to more efficient and safer operations. In conclusion, this study highlights the potential of AI to revolutionize marine vehicle identification and control, with implications for enhanced security, efficiency, and sustainability in maritime operations. It is advisable to conduct additional research to improve these models, enabling their application across a wider range of marine environments.

Abstract Abstract—This paper presents an intelligent non-player char- acter(NPC) for a survival game with a modified Q-learning-based scheme. Due to the dynamics of the computer game environment, reinforcement learning is employed to make this agent smart. This leads the agent to react appropriately based on the game’s scenario by choosing an action that provides a higher reward in the current situation. This is like a brain for the target NPC that processes different situations and reacts appropriately. Our intelligent agent is applied to a sample survival game with different complexity levels. In this game, multiple characters and objects alongside win-and-lose scenarios are considered. Our designed intelligent NPC is equipped with modified Q-learning to interact and try different actions on objects and learn about them. This learning process leads to an experience saved in the designed agent to react best to the environment. The efficiency of our proposed approach is evaluated through multiple scenarios and the appropriate reaction of the NPC is verified.

Abstract Abstract— This research proposes a novel approach to create a foundation for dynamic difficulty adjustment (DDA) within computer games that use procedural content generation (PCG), utilizing imitation learning to optimize gameplay. When PCG is used in creating the levels and enemies within a game, the difficulty adjustment must be ensured so that the game is not too hard or too easy for each player. However, PCG is random by nature, and thus, the developers may have a challenging task of adjusting the difficulty for each player in such games. The study aims to address these limitations by developing a foundation for DDA models based on imitation learning. The proposed model incorporates an imitation learning component, referred to as the 'Clone,' which replicates the player’s behavior, alongside an enemy creator agent that leverages procedural content generation (PCG) to design enemies. By analyzing the Clone's performance against these procedurally generated enemies, the system ensures the creation of fair and engaging levels. To this end, a 2D platformer Unity game using PCG was developed, and imitation learning was utilized through Unity's ML-agents module. These models were used to mimic the players' play-style to predict the player's performance in PCG-generated levels. Three separate models were created to mimic five players. It was observed that two of these models could mimic players' performance, showing that this method can be used to implement DDA.

Abstract Abstract— The brain is a complex organ that undergoes changes with age, and predicting brain age is crucial for monitoring brain health. It provides valuable insights into brain function and helps in the prevention of neurological diseases. This research predicts brain age through age classification based on fMRI data from the HCP dataset, consisting of individuals aged 22 to 36 years. After training a graph convolutional neural network, the model achieved an accuracy of 0.73 on the test data, demonstrating an improvement over previous studies on the same dataset. An evolutionary approach was then applied to optimize the selection of brain regions using a Genetic Algorithm to identify important and informative regions. This selection and optimization process maintained good predictive accuracy while reducing the number of brain regions. The results indicate that, despite using only half the original number of brain regions (8 regions), the model's accuracy remained at 0.65, showing only a slight decline. This highlights the significance of these regions in brain age classification. Identifying these key regions can contribute to the early diagnosis of brain and neurological diseases, enabling experts to better understand and manage the brain aging process.

Abstract Abstract— Deep learning has emerged as a transformative technology in ophthalmology, addressing critical challenges in the diagnosis and management of eye diseases such as diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), and central serous chorioretinopathy (CSCR). These conditions, among the leading causes of preventable blindness, require accurate and timely detection, which is often limited by traditional diagnostic methods due to inefficiency and the complexity of interpretation. The goal of this study is to examine the applications of deep learning in the diagnosis of ophthalmic diseases and to help researchers gain a better understanding of recent advances in model development, identify challenges associated with widespread implementation of these models in real-world applications, and outline future research directions in this area. Methodologically, recent studies using convolutional neural networks (CNNs), vision transformers, and hybrid models demonstrate high diagnostic accuracy and potential for early disease detection. Applications extend beyond disease diagnosis to lesion segmentation, disease progression monitoring, and personalized treatment planning. Deep learning systems have demonstrated comparable or superior diagnostic performance to human experts in detecting diseases such as DR and glaucoma. Despite these advances, challenges remain, including limited generalizability, data bias, and the need for explainable AI models to foster clinical trust and adoption. Addressing these challenges through improved model transparency, diverse datasets, and ethical frameworks will be critical to integrating deep learning into routine ophthalmic practice. This review highlights the significant advances in deep learning-driven ophthalmology and outlines a path for future research to optimize its clinical implementation.

Abstract Abstract— Voice Pathology Detection (VPD) aims to identify voice impairments through the analysis of speech signals, providing a foundation for developing diagnostic tools in advanced healthcare services to the public. This paper contributes to the development of efficient and accurate models based on deep learning (DL) for automatic VPD using sustained vowels of speech data. Therefore, this study explores the comparative efficacy of Mel-Frequency Cepstral Coefficients (MFCCs) and Linear Predictive Coding (LPC) as acoustic features extracted from vowels /i/, /a/, and /u/. Using the AVFAD database, we utilized and optimized a Convolutional Neural Network (CNN) as a DL model to classify healthy and pathological voices, prioritizing both accuracy and computational efficiency for real-time applications. Our findings reveal that 20 MFCC features extracted from vowel /i/ achieve the highest accuracy, with the optimal model reaching approximately 88% on test data. Our findings reveal that 20 MFCC features extracted from vowel /i/ achieve the highest accuracy, with the optimal model reaching approximately 88% on test data.

Abstract Abstract—Extracting organizational duties from legal documents is a critical yet challenging task, particularly in low-resource languages like Persian. This paper presents an innovative approach that integrates state-of-the-art Named Entity Recognition (NER) with advanced segmentation techniques and Large Language Models (LLMs) to accurately identify and extract duties assigned to organizations from Persian legal texts. Leveraging the power of the BERT-based model for NER, we enhance the recognition of relevant entities and ensure precise linkage to target organizations. Our method involves segmenting documents into sentences with an enhanced POS-based tokenizer, followed by the retrieval of contextually relevant segments based on the detected entities. We then explore the effectiveness of different LLM configurations, including a hierarchical approach that leverages both small and large models. Our experiments demonstrate that the hierarchical approach, combining ’Llama-3.1-8B’ and ’gpt-4o’, achieves an F1-score of 0.7901, significantly outperforming single-model approaches. This research underscores the potential of LLMs in legal text analysis, paving the way for more advanced tools in Natural Language Processing. Future work will include testing on a broader range of organizations, refining prompt engineering techniques, and enhancing model interpretability.

Abstract Abstract— Performance-based design of bridges requires prediction of different damage states of components. (RC) piers are key components in bridge system, which may experience severe damages during earthquakes. Therefore, seismic damage assessment of RC bridges depends strongly on predicting failure modes RC piers. Using machine learning for damage evaluation of structures is becoming increasingly popular in earthquake engineering. This study implements three different machine learning techniques to capture different damage limit states of RC bridge piers under seismic loading. For this purpose, three machine learning techniques including K-Nearest Neighbors (KNN), Artificial Neural Networks (ANNs) and decision tree regressions were utilized for predicting four damage states of a RC bridge piers tested experimentally under seismic excitations based on drift limits. The efficiency of the three algorithms in damage prediction of RC piers were compared.

The efficiency of the three algorithms in damage prediction of RC piers were compared. The efficiency of the three algorithms in damage prediction of RC piers were compared.

Abstract Abstract—Traditional Long Short-Term Memory (LSTM) networks are effective for handling sequential data but have limitations such as gradient vanishing and difficulty in capturing long-term dependencies, which can impact their performance in dynamic and risky environments like stock trading. To address these limitations, this study explores the usage of the newly introduced Extended Long Short-Term Memory (xLSTM) network in combination with a deep reinforcement learning (DRL) approach for automated stock trading. Our proposed method utilizes xLSTM networks in both actor and critic components, enabling effective handling of time series data and dynamic market environments. Proximal Policy Optimization (PPO), with its ability to balance exploration and exploitation, is employed to optimize the trading strategy. Experiments were conducted using financial data from major tech companies over a comprehensive timeline, demonstrating that the xLSTM-based model outperforms LSTM-based methods in key trading evaluation metrics, including cumulative return, average profitability per trade, maximum earning rate, maximum pullback, and Sharpe ratio. These findings mark the potential of xLSTM for enhancing DRL-based stock trading systems.