Journal of Applied Artificial Intelligence <p>Journal of Applied Artificial Intelligence (JAAI) is an international and interdisciplinary scholarly peer reviewed journal on artificial intelligence published by Saba Publishing.<br />JAAI devoted entirely to Artificial Intelligence and welcomes papers in the overall field including, but not limited to, machine learning and cognition, deep learning, supervised learning, unsupervised learning, classification, regression, clustering, big and streaming data, optimization algorithms, feature selection and extraction, pattern recognition, bio-informatics, uncertain information processes, recommender systems, E-service personalization, distributed and parallel processing, computer vision, neural networks, natural language processing, heuristic search, multi-objective optimization, multi-agent systems, advances in social network systems, reasoning under uncertainty, forecasting and predication models as well as other hot topics.</p> <p><strong>Editor in Chief: <a href="" target="_blank" rel="noopener">Dr Nibras Abdullah</a></strong><br /><strong>ISSN (online)</strong>: <a href="" target="_blank" rel="noopener">2709-5908</a><br /><strong>Frequency:</strong> Semiannual</p> Saba Publishing en-US Journal of Applied Artificial Intelligence 2709-5908 Integrative Approaches for Advancing Organoid Engineering: From Mechanobiology to Personalized Therapeutics <p>This research manuscript aims to explore the integration of cutting-edge technologies in the field of organoid engineering for applications in personalized precision medicine. The research investigative exploration will delve into the multifaceted aspects of organoid research, incorporating mechanobiological modulation, ultrasound stimulation, and acoustofluidics to enhance the engineering of organoids. The focus will extend to the development of organoids-on-a-chip platforms with integrated biosensors, providing real-time monitoring capabilities for improved disease modeling and drug testing. Additionally, the manuscript will address the challenges and opportunities associated with large-scale manufacturing of organoids, emphasizing the scalability of regenerative medicine approaches. The proposed research will contribute to the advancement of 3D tissue models, micro physiological systems, and multi-organoid systems, offering a very comprehensive perspective on the potential of these systematic technologies in reshaping the landscape of personalized medicine.</p> Zarif Bin Akhtar Anik Das Gupta Copyright (c) 2024 Journal of Applied Artificial Intelligence 2024-03-20 2024-03-20 5 1 1 27 10.48185/jaai.v5i1.974 Utilizing Deep Learning Methods for Heart Defect Identification via Electrocardiogram (ECG): A Literature Review <p>In recent years the application of Deep Learning is widely used in various fields of science, such as in the military, agriculture, health and even in other fields. In the field of health many studies use deep learning, especially related to heart disorders. Identification and detection of heart defects that are widely used are by using an electrocardiogram (ECG). Research related to ECG and the application of deep learning methods is very interesting for researchers, because researchers trace that there are still few studies that focus on researching related to it. This article aims to explain the trend of ECG research using deep learning approaches in recent years. We reviewed journals with the keyword title "ECG Deep Learning" and published from 2016 to October 2023. The articles that have been obtained are then classified based on the most frequently discussed topics including: data sets, pre-processing, feature extraction, and classification/identification methods. The approach used by some researchers is mostly to get the best results from the use of deep learning methods. This article will provide further explanation of the most widely used algorithms for ECG research with a deep learning approach. Of the deep learning methods used, almost 84% use the Convolutional Neural Network method. In this article, critical aspects of ECG research can be carried out in the future, namely the use of data in the form of other data from ECG, and the use of deep learning is a very big opportunity for researchers in the future.</p> Darwan Faiz Muqorrir Kaffah Heru Purnomo Kurniawan Lia Farhatuaini Ibnu Rusydi Copyright (c) 2024 Journal of Applied Artificial Intelligence 2024-03-20 2024-03-20 5 1 28 40 10.48185/jaai.v5i1.1007 An Improved Index Price/Movement Prediction by using Ensemble CNN and DNN Deep Learning Technique <p>As it knows prediction is always a challenging task in all terms. The goal of any stock prediction method is to develop a robust method for predicting trading price that can be used to improve investment decisions and accurate models. The paper proposes a hybrid model that combines the strengths of deep learning models CNN and DNN, and to develop a comprehensive methodology for the prediction of stock/index prices on Banknifty (NSE Bank), a highly volatile Indian sectorial Index that represents 12 major banks of the country. The hybrid model consists of two main components a CNN for feature extraction and a DNN for regression or classification tasks. In the context of stock price prediction, CNN layers can be used to extract features from input data (such as stock prices and indicators) related to an estimated future value. DNN layer can be used to combine features learned from the CNN layers. Model performance will be evaluated using various metrics including Accuracy, Precision, Recall, Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared. The method also analyses the impact of various factors on stock prices, including market volatility, economic indicators, and geopolitical events. The achieved accuracy of 97.48% indicates that the model was successful in accurately predicting the stock prices of Bank Nifty. The proposed method is expected to provide investors and financial analysts with a valuable tool for making informed investment decisions.</p> Jitendra Sisodia amar nayak Rajesh Boghey Copyright (c) 2024 Journal of Applied Artificial Intelligence 2024-03-20 2024-03-20 5 1 41 53 10.48185/jaai.v5i1.980 SMRD: A Novel Cyber Warfare Modeling Framework for Social Engineering, Malware, Ransomware, and Distributed Denial-of-Service Based on a System of Nonlinear Differential Equations <p>Cyber warfare has emerged as a critical aspect of modern conflict, as state and non-state actors increasingly leverage cyber capabilities to achieve strategic objectives. The rapidly evolving cyber threat landscape demands robust and adaptive approaches to protect against advanced cyberattacks and mitigate their impact on national security. Traditional cyber defense strategies often struggle to keep pace with the rapidly changing threat landscape, resulting in the need for more robust and adaptive approaches to protect against advanced cyberattacks. This paper presents a novel cyber warfare modeling framework, Social Engineering, Malware, Ransomware, and Distributed Denial-of-Service (SMRD), capturing the interactions and interdependencies between these core components. The SMRD framework offers insights for enhancing cyber defense, threat prediction, and proactive measures. A mathematical model consisting of a system of nonlinear differential equations is proposed to quantify the relationships and dynamics between the components.</p> Mohamed Aly Bouke Azizol Abdullah Copyright (c) 2024 Journal of Applied Artificial Intelligence 2024-03-20 2024-03-20 5 1 54 68 10.48185/jaai.v5i1.972 Gemini versus ChatGPT: applications, performance, architecture, capabilities, and implementation <p>This research paper presents an in-depth comparative examination of Gemini and ChatGPT, two prominent conversational AI models, exploring their respective applications, performance metrics, architectural variances, and overall capabilities. As conversational AI becomes increasingly prevalent across industries, comprehending the nuances of these models becomes pivotal for effective deployment. The paper initiates by outlining the wide array of applications for both Gemini and ChatGPT, spanning industries such as customer service, construction, finance, education, healthcare, and entertainment. It analyzes how each model addresses specific use cases, emphasizing their flexibility and potential impact across different sectors. Following this, the study assesses the performance of Gemini and ChatGPT through both empirical benchmarks and real-world deployment scenarios. Key metrics, including response coherence, accuracy, latency, and scalability, are scrutinized to gauge the models' ability to generate contextually appropriate and coherent responses in conversational contexts. Moreover, the paper elucidates the architectural distinctions between Gemini and ChatGPT, covering variances in training methodologies, model architectures, and underlying technologies. Understanding these architectural nuances provides deeper insights into the computational mechanisms underpinning each model's performance. Lastly, the paper explores the capabilities of Gemini and ChatGPT in handling complex linguistic phenomena, deciphering user intents, and sustaining engaging dialogues over prolonged interactions. This discussion encompasses language generation, sentiment analysis, context retention, and ethical considerations, shedding light on the potential of these models to facilitate meaningful human-computer interactions. Through this thorough comparative analysis, the research contributes to the ongoing conversation surrounding conversational AI systems. It offers valuable insights into the strengths and limitations of Gemini and ChatGPT, empowering stakeholders to make informed decisions regarding their optimal utilization across diverse applications.</p> Nitin Rane Saurabh Choudhary Jayesh Rane Copyright (c) 2024 Journal of Applied Artificial Intelligence 2024-03-20 2024-03-20 5 1 69 93 10.48185/jaai.v5i1.1052