IAES International Journal of Artificial Intelligence (IJ-AI)
IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; etc.
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<![CDATA[Deep learning model for detection acute cardiogenic pulmonary edema in cases of preeclampsia ]]>
<![CDATA[Hayat, Cynthia]]>;
<![CDATA[Soenandi, Iwan Aang]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i4.pp4806-4812
<![CDATA[The physiological changes during the pregnancy period increase the risk of developing pulmonary edema and acute respiratory failure. This condition falls under critical medical emergencies associated with maternal mortality. This study utilized a convolutional neural networks (CNN) architectural model employing chest Xray dataset images. CNN utilizes the convolution process by moving a convolutional kernel of a certain size across an image, allowing the computer to derive new representative information from the multiplication of portions of the image with the utilized filter.To simplify, the vanishing gradient issue occurs when information dissipates before reaching its destination due to the lengthy path between input and output layers. This study was developed model for detection acute cardiogenic pulmonary Edema in pre-eclampsia cases using chest Xray images, implemented using PyTorch, Keras, and MxNet. The validated model achieved its optimum with accuracy 90.65% and binary cross-entropy loss (BCELoss) value of 0.4538. It exhibited an improved sensitivity value of 83.514% using a 5% dataset and a specificity value of 57.273%. This indicates an increase in sensitivity value by 83.514% using a 5% data set and a specificity value of 57.273%. The research results demonstrate an improvement in accuracy compared to several similar studies that also utilized CNN models.]]>
<![CDATA[Innovative machine learning approaches for prediction of hypoglycemia in patients with type 2 diabetes ]]>
<![CDATA[Ramnath Gaikwad, Sachin]]>;
<![CDATA[Devi, Seeta]]>;
<![CDATA[Shekhar, Sameer]]>;
<![CDATA[Dumbre, Dipali]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i4.pp4453-4471
<![CDATA[Medical data science advances using machine learning, which predicts glucose levels. A supervised machine learning technique is employed in which regression and classification methods are used to check the prediction performance. The unsupervised machine learning technique makes clusters based on variables' similarities. Furthermore, the prediction accuracy of conventional machine learning techniques is improved by proposing a transfer learning technique. Based on a median value of 67 mg/dL, the data set is divided into two groups: group 1 (BSL 57 mg/dL to 67 mg/dL) has 50.67% of the samples, and group 2 (with BSL 68 mg/dL to 79 mg/dL) has 49.33% of the samples. In regression analysis, 5-fold cross-validation is performed. The decision tree (DT) and gradient boosting (GB) individually provide a prediction accuracy of 18.2%. Regarding classification analysis, a 10-fold cross-validation configuration is used for training and testing the model. AdaBoost, GB, random forest, and neural network achieve an accuracy rate of 66.3% and an area under curve (AUC) score of 0.731. In unsupervised learning, the datasets are divided into three clusters. The clustering result is used in regression and classification models using transfer learning. The accuracy and precision of the AdaBoost and GB are as follows: 69.6%, 0.696 with f1 0.661 and 69.6%, 0.708 with f1 0.708, respectively.]]>
<![CDATA[Abnormality-aware bone fracture detection and classification using the triple context attention model ]]>
<![CDATA[Sultana, Tabassum Nahid]]>;
<![CDATA[Parveen, Asma]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i4.pp4667-4674
<![CDATA[In this study, a novel approach is introduced for fracture detection in bone x-ray images, introducing the triple context attention model (TCAN) that combines concentrated extensive convolutional segments with an attention mechanism to enhance positional data. The TCAN model significantly improves fracture recognition accuracy while reducing model complexity. Leveraging a diverse dataset, consistently achieving high accuracy levels across various body parts. By addressing, mislabelling issues, and employing a visual attention network (VAN), to refine the model's performance. The TCAN model emerges as a robust, computationally efficient solution, offering a remarkable average accuracy of 97.86%. This study contributes valuable advancements to medical imaging and diagnostics, providing a highly effective tool for skeletal fracture detection.]]>
<![CDATA[Obstructive sleep apnea detection based on electrocardiogram signal using one-dimensional convolutional neural network ]]>
<![CDATA[Widadi, Rahmat]]>;
<![CDATA[Rizal, Achmad]]>;
<![CDATA[Hadiyoso, Sugondo]]>;
<![CDATA[Fauzi, Hilman]]>;
<![CDATA[Said, Ziani]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i4.pp4129-4137
<![CDATA[Obstructive sleep apnea (OSA) is a respiratory obstruction that occurs during sleep and is often known as snoring. OSA is often ignored even though it can cause cardiovascular problems. Early diagnosis is needed for prevention towards worse complications. OSA clinical diagnosis can use polysomnography (PSG) while the patient is sleeping. The PSG examination includes calculating total apnea plus hypopnea every hour during sleep. However, PSG examination tends to be high cost, takes a long time, and is impractical. Since OSA is related to breathing and heart activity, the electrocardiogram (ECG) examination is an alternative tool in OSA analysis. Therefore, this study proposes OSA detection on single lead ECG using one dimensional (1D)-convolutional neural network (CNN). The proposed CNN architecture consists of 4 convolutional layers, 4 pooling layers, 1 dropout layer, 1 flatten layers, 2 dropout layers, 1 dense layer with rectified linear unit (ReLU) activation function, and 1 dense layer with SoftMax activation function. The proposed method was then tested on the ECG sleep apnea dataset from PhysioNet. The proposed model produces an accuracy of 92.69% with the average pooling scenario. The proposed method is expected to help clinicians in diagnosing OSA based on ECG signals.]]>
<![CDATA[Harnessing adapted capsule networks for accurate lumpy skin disease diagnosis in cattle ]]>
<![CDATA[Mallikarjun, Goddeti]]>;
<![CDATA[Narayana, V. A.]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i4.pp3909-3919
<![CDATA[Lumpy skin disease (LSD) poses a substantial risk to livestock, emphasizing the critical need for reliable diagnostic tools to ensure timely intervention. The considerable economic impact of LSD further accentuates the imperative for efficient diagnosis. In this context, artificial intelligence (AI) emerges as a transformative solution, playing a pivotal role in providing swift detection capabilities. Rapid identification of LSD not only alleviates economic burdens but also impedes the disease’s propagation with in herds. A ground breaking in iterative involves the implementation of an adapted capsule network (CapsNet) expressly designed for diagnosing LSD. This innovative strategy is finely tuned to discern intricate patterns in disease manifestation, achieving an impressive accuracy rateof 97.6%. The model’s effectiveness is evident in it is capacity to differentiate between infected and healthy cases, with precision, recall, and F1 score metrics registering at 9.65%, 97.1%, and 96.3%, respectively. This high level of precision underscores the model’s proficiency in minimizing errors, solidifying its role as a dependable tool for precise LSD diagnosis and intervention, contributing significantly to the overall health and economic well-being of livestock populations.]]>
<![CDATA[Design of an effective multiple objects tracking framework for dynamic video scenes ]]>
<![CDATA[Kumar Karanam, Sunil]]>;
<![CDATA[Pokale Kavya, Narasimha Murthy]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i4.pp3879-3891
<![CDATA[Nowadays, the applications corresponding to video surveillance systems are getting popular due to their wide range of deployment in various places such as schools, roads, and airports. Despite the continuous evolution and increasing deployment of object-tracking features in video surveillance applications, the loopholes still need to be solved due to the limited functionalities of video-tracking systems. The existing video surveillance systems pose high processing overhead due to the larger size of video files. However, the traditional literature report quite sophisticated schemes which might successfully retain higher object detection accuracy from the video scenes but needs more effectiveness regarding computational complexity under limited computing resources. The study thereby identifies the scope of enhancement in traditional object-tracking functions. Further, it introduces a novel, cost-effective tracking model based on Gaussian mixture model (GMM) and Kalman filter (KF) that can accurately identify numerous mobile objects from a dynamic video scene and ensures computing efficiency. The study's outcome shows that the proposed strategic modelling offers better tracking performance for dynamic objects with cost-effective computation compared to the popular baseline approaches.]]>
<![CDATA[Framework for abnormal event detection and tracking based on effective sparse factorization strategy ]]>
<![CDATA[Divyaprabha, Divyaprabha]]>;
<![CDATA[Seebaiah, Guruprasad]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i4.pp3900-3908
<![CDATA[The idea of tracking video objects has evolved to facilitate the area of surveillance systems. However, most current research efforts lie in speedy abnormal event detection and tracking of objects of interest tracking. However, the primary challenge is dealing with complex video structures' inherent redundancy. The existing research models for video tracking are more inclined towards improving accuracy. In contrast, the consideration of a more significant proportion of mobile object dynamics, e.g. abnormal events, in motion over the crowded video frame sequence is mainly overlooked, which is essential to study a specific movement pattern of the object of interest appearing in the frame sequence concerning the cost of computation factors. The study thereby introduces a unique strategy of speedy abnormal event detection and tracking, which facilitates video tracking to assess a specific pattern of object of interest movement over complex and crowded video scenes, considering a unique learning-based approach. The extensive simulation outcome further shows that the proposed tracking model accomplishes better tracking accuracy yet retains an optimized computation cost compared to the baseline studies. The computation of video tracking also accomplishes higher detection rates even in the challenging constraints of partial/complete occlusion, illumination variation and background clutter.]]>
<![CDATA[Impact of federated learning and explainable artificial intelligence for medical image diagnosis ]]>
<![CDATA[Muthuramalingam, Sivakumar]]>;
<![CDATA[Thiyagarajan, Padmapriya]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i4.pp3772-3785
<![CDATA[Medical image recognition has enormous potential to benefit from the recent developments in federated learning (FL) and interpretable artificial intelligence (AI). The function of FL and explainable artificial intelligence (XAI) in the diagnosis of brain cancers is discussed in this paper. XAI and FL techniques are vital for ensuring data ethics during medical image processing. This paper highlights the benefits of FL, such as cooperative model training and data privacy preservation, and the significance of XAI approaches in providing logical justifications for model predictions. A number of case studies on the segmentation of medical images employing FL were reviewed to compares and contrasts various methods for assessing the efficacy of FL and XAI based diagnostic models for brain tumors. The relevance of FL and XAI to improve the accuracy and interpretability during medical image diagnosis have been presented. Future research directions are also described indicating as to integrate data from various modes, create standardised evaluation processes, and manage ethical issues. This paper is intended to provide a deeper insight on relevance of FL and XAI in medical image diagnosis.]]>
<![CDATA[Development of a prioritized traffic light control system for emergency vehicles ]]>
<![CDATA[Oluwatobi Aworinde, Halleluyah]]>;
<![CDATA[Emmanuel Adeniyi, Abidemi]]>;
<![CDATA[Adebayo, Segun]]>;
<![CDATA[Adeniji, Faith]]>;
<![CDATA[Julius Aroba, Oluwasegun]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i4.pp4019-4028
<![CDATA[This research presents a model for an adaptive traffic signal control system aimed at improving urban traffic regulation. It dynamically adjusts signal timing based on vehicle volume at intersections, prioritizing emergency vehicles by allowing them immediate passage. Utilizing Arduino coding, the system controls traffic light intensity according to the traffic flow, enhancing road safety and efficiency. This innovative approach not only facilitates faster clearance for emergency services without human intervention but also reduces congestion and accident rates. This research creates a model for a prioritized traffic signal control system. When the vehicular volume at the intersection varies, the signal time alters autonomously. It identifies the ambulance/emergency vehicles and allows the green light for emergency vehicles like ambulances, and fire engines. This approach may be used to detect traffic accidents and infractions of automobile spiral motions. When erected on the road, the entire system allows for quick traffic clearing for rescue vehicles without requiring a policeman. The system's design eliminates the need for sensors or radio frequency identification (RFID) tags, simplifying traffic management. Simulations validate that emergency vehicle travel time is significantly reduced, proving the system's effectiveness in streamlining urban traffic flows.]]>
<![CDATA[Transfer-learning based skin cancer diagnosis using fine-tuned AlexNet by Marine Predators Algorithm ]]>
<![CDATA[Ibrahim Khaleel, Maha]]>;
<![CDATA[Lakizadeh, Amir]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v13.i4.pp4822-4832
<![CDATA[Melanoma represents one of the most dangerous manifestations of skin cancer. According to statistics, 55% of patients with skin cancer have lost their lives as a result of this disease. Early diagnosis of this condition will significantly reduce mortality rates and save lives. In recent years, deep learning methods have shown promising results and captured the attention of researchers in this field. One common approach is the use of pre-trained deep neural networks. In this work, a pre-trained AlexNet networks, which are networks with specified architecture and weights is used to automatic skin melanoma diagnosis. In the transfer learning phase, by reducing the learning rate, the pre-trained network is trained to recognize Skin cancer, which is called fine-tuning. In addition, Hyperparameters of the AlexNet network have been optimized by the Marine Predators Algorithm (MPA) to enhance the network performance. Experimental findings show the satisfactory efficiency of the presented approach, with an accuracy rate of 98.47%. The outcomes demonstrate the effectiveness of the suggested approach in contrast to alternative existing methods.]]>
