Journal of Innovations in Computer Science and Engineering
Year:2024 | Volume:2 | Issue:1|Papers Count:12

Emotion recognition in text is a growing area in Natural Language Processing ( NLP ), essential for improving human-computer interactions by allowing systems to interpret emotional expressions. While much progress has been made in English, Persian emotion recognition has seen limited devel- opment due to resource constraints and linguistic challenges. In this study, we address these gaps by leveraging two key Persian datasets, ArmanEmo and ShortEmo, to train an efficient emotion recognition model. Using FaBERT, a BERT-based model optimized for Persian, we employ interme- diate fine-tuning on a large collection of informal and formal Persian texts to enhance the model’s adaptability to colloquial language. This step significantly improves comprehension of Persian textvariations, as reflected in reduced perplexity scores. Our final evaluations, incorporating accuracy, precision, recall, and F1 score metrics, demonstrate that this fine-tuned FaBERT model achieves strong performance in emotion recognition, providing a promising approach for NLP in low-resource languages...

Abstract— Intent detection and slot filling are crucial for understanding human language and are essential for creating intelligent virtual assistants, chatbots, and other interactive systems that interpret user queries accurately. Recent advancements, especially in transformer-based architectures and large language models (LLMs), have significantly improved the effectiveness of intent detection and slot filling. This paper, proposes a method for effectively utilizing low volume fine-tuning data samples to enhance the natural language comprehension of lightweight language models, yielding a nimble and efficient approach. Our approach involves augmenting new data while increasing model layers to enhance understanding of desired intents and slots. We explored various synonym replacement methods and prompt-generated data samples created by large language models. To prevent semantic meaning disturbance, we established a lexical retention list containing non-O slots to preserve the sentence's core meaning. This strategy enhances the model's slot precision, recall, F1-score, and exact match metrics by 1.41%, 1.8%, 1.61%, and 3.81%, respectively, compared to not using it. The impact of increasing model layers was studied under different layer arrangement scenarios. Our results show that our proposed solution outperforms the baseline by 10.95% and 4.89% in exact match and slot F1-score evaluation metrics.

Structural reliability analysis (SRA) is associated with complex calculations and large number of simulations. In this paper, machine learning (ML) methods are integrated with SRA to reduce the overall intricacy and computational cost of direct SRA methods, such as the Monte Carlo simulation (MCS) method. An SRA is conducted in this paper on post-tensioned concrete members under the influence of prestress loss, and their reliability indices are obtained through the MCS method. The results of the SRA are used to create a database for data fitting of the ML algorithms. The algorithms are compared to find the most accurate ML model to be applied on the problem at hand. For the SRA, different stochastic parameters with specified probabilistic distributions are considered for the numerical models, and nonlinear dynamic analyses are conducted on them. Using the labeled data resulted from the SRA, five ML algorithms are compared; (i) linear regression, (ii) random forest, (iii) artificial neural network, (iv) k-nearest neighbors, (v) extreme gradient boosting. R-squared and root mean squared error are considered as the metrics used for the comparison of the ML models. Bayesian search is used for hyperparameter optimization of algorithms. The performance of the linear regression algorithm (R2=0.67 and RMSE=0.26) indicates that the SRA problems are highly nonlinear and linear algorithms cannot precisely map the relationships in data. However, the results show that extreme gradient boosting has the finest accuracy with R2=0.9 and RMSE=0.04. Additionally, its predicted values mostly have relative errors of less than ±30%.

Abstract— This study examines the impact of using artificial intelligence chatbots on improving the quality of life for women with multiple sclerosis (MS) in Iran. Using a qualitative method and semi-structured interviews, the experiences of women participating in relation to the functionality of AI chatbots were analyzed. The findings indicate that chatbots can play a significant role as supportive and informational tools in managing the disease, reducing anxiety, and improving communication for these women. Additionally, these tools assist in organizing daily tasks and reducing feelings of loneliness. Although some participants pointed to an excessive reliance on chatbots, overall, the results show more positive effects compared to the disadvantages of this technology. Ultimately, the research suggests that future studies should explore the psychological and ethical impacts of using chatbots more deeply. Ultimately, the research suggests that future studies should explore the psychological and ethical impacts of using chatbots more deeply. Ultimately, the research suggests that future studies should explore the psychological and ethical impacts of using chatbots more deeply.

Parkinson's Disease (PD) is a prevalent neurological disorder marked by motor symptoms such as rigidity and tremors. Accurate and timely assessment of disease severity is essential for judging the efficacy of various treatment interventions. This study presents an innovative approach that employs computer vision technology paired with advanced deep learning techniques to enable precise evaluations of Parkinson's severity.Leveraging the high accuracy of Kinect cameras in capturing essential movement patterns, our proposed system employs advanced convolutional neural networks, specifically incorporating mechanisms from the Spatial-Temporal Graph Convolutional Network (STGCN) and the Two-Stream Adaptive Graph Convolutional Network (2SAGCN). These architectures are adept at detecting movement anomalies and generating precise quantitative severity measures. To further enhance the performance of the 2SAGCN, we introduce distinct temporal and spatial attention modules, resulting in improved classification outcomes. The model achieves outstanding metrics, with accuracy, precision,recall,and F1 score recorded at 94.14 ± 0.26, 98.1 ± 0.12, 98.6 ± 0.05,and98.2 ± 0.02, respectively The severity classification framework distinguishes between11specific classes of Parkinson's symptoms, which are derived from 9 distinct motion categories.Within this framework, class 0 represents healthy individuals, while classes 0 to 1 correspond to varying degrees of severity in Parkinson's symptoms, resulting in a comprehensive classification system encompassing 99 distinct outcomes.To further enhance the model’s accuracy, we have implemented strategies such as transfer learning and data 3D augmentation. This research marks a significant step forward in the realm of non-invasive, quantitative assessments of Parkinson's Disease, showcasing the potential of cutting-edge technology and state-of-the-art neural network architectures.

Abstract—The paper aimed to analyze the thematic evolution and various networks of intellectual structures in the field of biomedical ontologies during 2014-2023. This applied research used an analytical and descriptive method, co-word techniques, and social network analysis. A web-based interface of bibliometrix, Microsoft Excel, and VOSviewer software were used for descriptive bibliometric study, data analysis, and network structure visualization. The period from mid-2020 to mid-2021 presented an increased dissemination of significant and prominent keywords within the overlay network in the field. Five major topic clusters were identified based on a co-occurrence network. These clusters labeled ‘gene ontology’, ‘biomedical informatics focusing on AI techniques’, ‘bioinformatics applications in biomarker discovery’, ‘protein interaction networks in Alzheimer's proteomics’, and ‘network-based molecular mechanism’. Basic clusters were ’gene ontology’, ‘bioinformatics’, and ‘gene expression’. Moreover, five clusters experienced significant developments between 2023 and 2024, namely ‘bioinformatics’, ‘deep learning’, ‘machine learning’, ‘transcriptome’, and ‘network pharmacology’. These topics are the latest and hottest concepts in this field. Clusters, namely ‘deep learning’,’ machine learning, and ‘ontology’ were recognized as niche and the most well-developed themes. The most mature and mainstream thematic clusters were namely ‘transcriptome’, ’prognosis’, and ‘rna-seq’. The most undeveloped and chaotic themes were ‘network pharmacology’ and ‘molecular docking’.

Abstract - As artificial intelligence (AI) increasingly becomes central to digital transformation, businesses across industries are recognizing its transformative potential for enhancing efficiency, accuracy, and innovation. This article examines a structured framework for AI integration that empowers businesses to manage the challenges associated with AI implementation, emphasizing both technical and "soft" competencies crucial for successful implementation. Through a phased approach, including Discovery, Roadmap Design, Implementation, and Evaluation, this review provides actionable insights to align AI solutions with business objectives, optimize resources, and overcome organizational barriers. The framework highlights how AI-driven tools, such as predictive analytics, data mining, and automated decision-making systems, enhance strategic capabilities, streamline operations, and improve customer engagement. To ensure long-term success, this study underscores the significance of cultivating an environment that promotes innovation and teamwork. AI adoption requires not only robust data infrastructure and technical expertise but also strategic foresight, cross-functional collaboration, and a commitment to iterative learning. By integrating technical and soft knowledge, organizations can overcome challenges in AI adoption, such as resistance to change and uncertain ROI, by fostering a supportive environment that enables AI-driven growth. This article provides decision-makers with a thorough guide, equipping them with the insight needed to maximize AI’s potential for long-term competitive success in an evolving digital world.

Abstract— Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and the accumulation of amyloid-beta plaques. Early detection is crucial for timely intervention, and the Aβ42/Aβ40 ratio is a key biomarker for identifying amyloid deposition. In this study, we propose a method to predict the Aβ42/Aβ40 ratio using the extracted features from MRI images using 3D Convolutional Neural Network (3D CNN). Moreover, Random Forest Regression is employed to obtain the relationship between MRI features and the Aβ42/Aβ40 ratio. Our results demonstrate a strong correlation (r = 0.72) between the predicted and actual Aβ42/Aβ40 ratios, effectively predicting amyloid accumulation. This result also makes the proposed feature extraction model more reliable. By leveraging MRI and molecular biomarkers such as the Aβ42/Aβ40 ratio, the proposed method provides valuable insights into disease progression and early diagnosis. By leveraging MRI and molecular biomarkers such as the Aβ42/Aβ40 ratio, the proposed method provides valuable insights into disease progression and early diagnosis.

Abstract— Telecommunications companies rely on recommendation systems to deliver personalized services and enhance customer satisfaction. Traditional methods, such as Collaborative Filtering (CF) and Content-Based Filtering (CBF), often fall short in capturing the complex relationships and social influences inherent in large telecom networks. In this paper, we propose a novel Graph Neural Network (GNN)-based recommendation system that integrates customer profiles with graph data representing customer interactions (e.g., calls, messages). The system uses the GraphSAGE architecture to aggregate information from each customer’s network, enabling it to learn from both direct and indirect relationships. By combining customer demographic and usage data with interaction networks, our model provides more accurate and personalized service recommendations. We evaluate the system on a real-world telecom dataset, comparing it with traditional models, including CF, CBF, and Matrix Factorization (MF). The GNN-based system achieves a significant performance boost, with a precision of 0.81 and an F1-score of 0.80, outperforming all baselines. These results highlight the ability of GNNs to capture social and communication patterns, making them highly effective for telecom recommendations. Future work will explore the scalability of the system and its application to real-time data, further enhancing its potential for customer retention and revenue growth.

Abstract—In today's world, simplifying compound and complex sentences into simple sentences is crucial for enhancing machine understanding in various natural language processing (NLP) tasks, such as inference, machine translation, and information extraction. This simplification process improves accuracy. Consequently, our research is inspired by a text simplification method called "split and rephrase." We introduce a new sequence-to-sequence text generation model that transforms complex sentences into simple ones based on the conjunction "and" in Persian. By utilizing linguistic models with millions or even billions of parameters, our approach facilitates a better understanding of text complexities and more accurate identification of breaking points. Our results show an output accuracy of 0.47 in the BLEU score for the generated simple sentences, which are both grammatically correct and fluent. By utilizing linguistic models with millions or even billions of parameters, our approach facilitates a better understanding of text complexities and more accurate identification of breaking points. Our results show an output accuracy of 0.47 in the BLEU score for the generated simple sentences, which are both grammatically correct and fluent.

Abstract— With the increase in textual data generated on the internet and the limited time individuals have for reading, the need for automatic text summarization is more essential than ever. One application of summarization is title generation. The goal of this study, which falls within the field of digital humanities and interdisciplinary studies, is to provide a framework for title generation through extractive and abstractive summarization methods, focusing specifically on chapters of the Qur'an. For extractive summarization, eleven different methods have been examined, some of which are novel and innovative. For the abstractive part and title generation, several models have been trained to select the most effective one. In this research, the Persian translation of the Qur'an is used as the primary source, and a dataset was created based on the first ten parts (juz) of the Qur'an, including extractive summaries, abstractive summaries, and titles for various sections of the chapters. The results of this study indicate that the titles generated through summarization are close to human-generated titles, based on BERTScore, R-1, R-2, and R-l values of 21.03, 6.85, 20.73, and 52.51, respectively. It is important to note in the evaluation that a single fixed title does not exist for a document; multiple titles may also be valid. In human evaluation, we observed that the average score produced by the proposed approach is 0.59, while for the best results from other approaches, this value is 0.44.

Abstract - - Abstract - - Abstract - This paper proposes a master-slave approach to simultaneously control two drones with the aim of carrying an object toward a goal. The proposed method utilizes the Double Deep Q-Learning (DDQN) technique to train a master agent to be able to carry an object toward a goal with the help of a slave agent. This procedure is implemented such that the master agent gathers the observations and specifies the actions to be made by itself and the slave agent. Indeed, the slave agent just applies a predefined action and does not process any input for producing the output. This manner of learning, leads to a unified convergence to an optimal solution compared to the situation in which each agent is trained separately. To verify the functionality of the proposed method, the algorithm is examined in the webots simulation environment. The simulations show that the introduced method has a good performance when controlling the drones to reach to the goal. The introduced method, other than algorithmic benefits which leads to a faster convergence of the model, suggests some reduction in the processing demand. The reason is that the learning procedure is guided by one of the agents and consequently only one of the agents is responsible for doing the calculations that lead to choosing the action. In this scenario, the slave agent does not require any processing resources for choosing the action and just simply applies a predefined action dictated by the master agent.