Association for Scientic Computing Electronics and Engineering (ASCEE): Open Journal Systems
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Classifying Gait Disorder in Neurodegenerative Disorders Among Older Adults Using Machine Learning
Full text linkGait disorders are a significant concern for older adults, particularly those with neurodegenerative diseases such as Parkinson’s disease, Huntington’s disease, and Amyotrophic Lateral Sclerosis. Accurately classifying these conditions using gait data remains a complex challenge, especially in older populations, due to age-related changes in gait patterns, comorbidities, and increased variability in mobility, which can obscure disease-specific characteristics. This study explicitly classifies neurodegenerative diseases in older adults by analysing age-specific gait force data. Continuous Wavelet Transform (CWT) was utilised for advanced feature extraction, capturing both temporal and spectral signal characteristics. Classifiers including Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Multilayer Perceptron (MLP) were employed. The results demonstrated that SVM achieved an accuracy of 87.5%, outperforming RF and MLP, which achieved 83.3% and 50.0%, respectively. These findings underscore the importance of using tailored machine learning approaches to improve the diagnosis and management of neurodegenerative diseases in older adults. The potential for real-world application includes integration into clinical settings, enabling early detection and personalized interventions for individuals with gait disorders
Deep Learning Approach to Lung Cancer Detection Using the Hybrid VGG-GAN Architecture
Full text linkLung cancer ranks among the primary contributors to cancer-related deaths globally, highlighting the need for accurate and efficient detection methods to enable early diagnosis. However, deep learning models such as VGG16 and VGG19, commonly used for CT scan image classification, often face challenges related to class imbalance, resulting in classification bias and reduced sensitivity to minority classes. This study contributes by proposing an integration of the VGG architecture and Generative Adversarial Networks (GANs) to improve lung cancer classification performance through balanced and realistic synthetic data augmentation. The proposed approach was evaluated using two datasets: the IQ-OTH/NCCD Dataset, which classifies patients into Benign, Malignant, and Normal categories based on clinical condition, and the Lung Cancer CT Scan Dataset, annotated with histopathological labels: Adenocarcinoma, Squamous Cell Carcinoma, Large Cell Carcinoma, and Normal. The method involves initial training of the VGG model without augmentation, followed by GAN-based data generation to balance class distribution. The experimental results show that, prior to augmentation, the models achieved relatively high overall accuracy, but with poor performance on minority classes (marked by low precision and F1-scores and FPR exceeding 8% in certain cases). After augmentation with GAN, all performance metrics improved dramatically and consistently across all classes, achieving near-perfect precision, TPR, F1-score, and overall accuracy of 99.99%, and FPR sharply reduced to around 0.001%. In conclusion, the integration of GAN and VGG proved effective in overcoming data imbalance and enhancing model generalization, making it a promising solution for AI-based lung cancer diagnostic systems
Experimental Analysis of Fresnel Lens-Based Solar Desalination Systems with Copper Receivers for Enhanced Thermal and Electrical Performance
Full text linkSolar desalination represents a breakthrough technology for creating sustainable freshwater because it meets both the water quality standards and technology efficiency requirements of modern times. The current desalination methods, which depend on fossil fuels, encounter major obstacles regarding their energy requirements and economical performance. The research investigates the improvement of solar desalination performance through coupling Fresnel lens technology with copper-based receivers to maximize thermal characteristics and power generation benefits. This research successfully unites Fresnel lenses of high performance with copper receivers to reach increased steam temperatures alongside power production during the same procedure. The research team performed experimental tests using a system that included four large Fresnel lenses in Sharjah, UAE. Under different operating settings, the system demonstrated its performance by measuring its flow rates together with ambient temperatures and recording the steam output values. The experimental data showed that bigger Fresnel lenses boosted the steam temperature beyond 1000°C as well as pushing pressure levels to 8 bar, which led to remarkable system efficiency benefits. The copper receiver system generated 775 mA DC electric current, which collectively enhanced the system's power efficiency. The tested combination of Fresnel lenses and copper receivers demonstrates an effective way to enhance solar desalination systems, according to observed experimental data. The dualfunction technology combines desalination efficiency improvement with electricity production capabilities to establish a sustainable freshwater production method for arid regions. This investigation creates a basis for developing economical renewable desalination systems going forward
Dynamic Ball Balancing Using Deep Deterministic Policy Gradient (DDPG)-Controlled Robotic Arm for Precision Automation
Full text linkThis paper presents a reinforcement learning (RL)-based solution for dynamic ball balancing using a robotic arm controlled by the Deep Deterministic Policy Gradient (DDPG) algorithm. The problem addressed is maintaining ball stability under external disturbances in automated manufacturing. The proposed solution enables adaptive, precise control on flat surfaces. The research contribution is a comparative evaluation of DDPG and Soft Actor-Critic (SAC) algorithms for trajectory control and stabilization. A simulated environment is used to train the RL agents across multiple initial ball positions. Key performance metrics-settling time, rise time, overshoot, and steady-state error-are analyzed. Results show DDPG outperforms SAC with smoother trajectories, ~25% faster settling times, and significantly lower overshoot and steady-state errors. Visual analysis confirms that DDPG consistently drives the ball to the center with minimal deviation. These findings highlight DDPG’s advantages in control accuracy and stability. In conclusion, the DDPG-based approach proves highly effective for precision automation tasks where fast, stable, and reliable control is essential
Analytical Investigation of the Existence and Ulam Stability of Integro-Differential Equations with Conformable Derivatives Under Non-Local Conditions
Full text linkThis study examines an integro-differential equation involving fractional conformable derivatives and non-local conditions. It proves the existence and uniqueness of mild solutions by applying the Banach fixed-point theorem. Furthermore, it demonstrates a notable result about the existence of at least one solution, backed by conditions based on the Krasnoselskii fixed-point theorem. The investigation also explores the Ulam stability of integro-differential equations. To highlight the practical relevance and robustness of the findings, an illustrative example is provided
Aesthetic interpretation of waruna symbolism in the colossal dance performance waruna rakta samasta
Full text linkThis research departs from the lack of scholarly exploration into how mythological figures like Waruna can function as aesthetic sources in contemporary Balinese performing arts. Waruna, known in Hindu-Balinese mythology as the god of water and the ocean, symbolizes cosmic harmony between nature and human life. This study aims to analyze how Waruna’s symbolism contributes to the aesthetic construction of the colossal dance performance Waruna Rakta Samasta, particularly in terms of form, meaning, and presentation. Employing a qualitative method, the research integrates Djelantik’s aesthetic theory focused on form, weight, and appearance, and a hermeneutic approach to interpret symbolic meaning. Data were collected through direct observation of the performance and literature studies. Findings reveal that Waruna's symbolism is not only embodied in visual elements like the Borobudur outrigger boat, lighting, and video mapping, but also in movement and music, producing an immersive maritime experience. The aesthetic is enriched with spiritual and philosophical messages that align with the Balinese Tri Hita Karana values. This study contributes to the development of colossal performing arts in Bali by offering a contemporary reinterpretation of traditional symbols and providing a model for integrating mythological aesthetics into modern performance practice
A Morphological Context Blocks Hybrid CNN for Efficient Acute Lymphoblastic Leukemia Classification
Full text linkAcute Lymphoblastic Leukemia (ALL) is an aggressive?hematologic malignancy that necessitates early and accurate diagnosis for improved therapeutic efficacy. Although it is a routine practice, the visual blood smear analysis is tedious and?subject to human inaccuracies. This paper proposes a novel morphology-guided deep learning approach called Morphological Context Blocks (MCB)-HyperNet embedding morphological operations into a hybrid CNN architecture. The CNN architectures depend mainly on automatic learning through convolutive filters, so they miss crucial?morphological features that distinguish between leukemic and normal cells. In this study, we propose a deep learning-based approach that directly incorporates morphological dilation?and erosion in the deep learning data pipeline to exploit the potential of morphological feature extraction for our specific task, resulting in enhanced accuracy and reduced diagnostic costs, which ultimately can improve patient outcomes. In addition, the computational efficiency and modularity of the MCB-HyperNet framework facilitate easy adaptation and scalability to many other medical imaging tasks, such as the classification of various diseases, except the classification of?leukemia. We trained the proposed MCB-HyperNet on different image resolutions from the ALL dataset (168×168, 224×224, 256×256), different batch sizes (16 and 32), and also different training epochs (30, 35, 40, 45, 50) to get the best hyperparameter configuration. The MCB-HyperNet takes advantage of the strong feature extraction ability of ResNet and the light computing resource of MobileNetV3, ultimately obtaining 99.69% accuracy, 98.78% precision, 99.49% sensitivity,?99.12% F1-score, and 99.78% specificity. This new integration greatly enhances the accuracy of early detection, minimizes diagnostic errors, and could have?significant clinical and economic advantages. MCB-HyperNet is a mini CNN, so it shows a good balance between efficiency and accuracy, making scalability and extensibility possible in more medical imaging tasks
Artificial Intelligence-Enhanced Sensorless Vector Control of Induction Motors Using Adaptive Neuro-Fuzzy Systems: Experimental Validation and Benchmark Analysis
Full text linkThis study addresses the limitations of traditional Model Reference Adaptive Systems (MRAS) in sensorless induction motor (IM) control, particularly the degraded performance at low speeds and under dynamic load conditions. The main objective is to enhance speed and torque estimation accuracy by replacing the classical proportional-integral (PI) adaptation mechanism with an adaptive neuro-fuzzy architecture. The research contribution lies in developing and experimentally validating two intelligent adaptation schemes: one based on fuzzy logic and another combining fuzzy inference with a recurrent neural network (RNN) within a sensorless field-oriented control (FOC) framework. The proposed system integrates a fuzzy logic-based estimator and an RNN-driven torque predictor to improve tracking precision and robustness. Real-time implementation was carried out on a 1.1 kilowatt, 1430 revolutions per minute induction motor using a dSPACE DS1104 platform. Comparative experiments were conducted under two challenging benchmark profiles that include load disturbances, parameter mismatches, and full-speed reversals. Results showed that the hybrid neuro-fuzzy controller reduced the steady-state speed error by 91 %, from 0.65 rad/s to 0.08 rad/s, and improved torque estimation accuracy by 42%, reducing SMAPE from 45.2 % to 26.3 %, compared to the PI-based MRAS. It also outperformed the standalone fuzzy and neural MRAS controllers in rise time, tracking error, overshoot suppression, and adaptation quality. These findings confirm that the proposed method provides improved estimation fidelity, enhanced control robustness, and reliable sensorless operation suitable for real-time industrial applications. The study concludes that the integration of neuro-fuzzy intelligence into MRAS-based control structures offers a technically effective and scalable solution for advanced IM drives
A YOLO-Based Target Detection Algorithm for DJI Tello Drone
Full text linkThe expansion of the application of drones has dispersed in wide range across military and civilian sectors. The application in such search and rescue missions are applicable with integration of computer vision and machine learning. A key feature of the drone for such applications is the capability to detect and locate objects and targets. Despite traditional methods perform excellently, deep-learning methods are the game changer in detection due to their better accuracy and robustness, rendering them ideal for real-time applications. The methods, including the YOLO series, are in continuous development to further enhance their performance. however, the regular issuance of updated and newer versions has intrigued curiosity regarding the potential superiority of the newer version over the previous versions in drone application. Hence, this paper has chosen the YOLOv8, YOLOv5u and YOLOv11 models for implementation on a DJI Tello drone to detect a custom target. A dataset for the target as a single class to be trained and validated is generated through images annotation. The target is required to be captured in the position of middle of the frame. However, the analysis upon performance metrics found that every model recorded high rates of precision, accuracy and recall. Yet, the simulations and experimentations displayed the ability of the model to accurately recognize the target. Thus, in order to evaluate the performance of each model thoroughly, it is advisable to ensure the data is sufficient and unbiased, while properly choosing the right setting parameters to the YOLO models
Improving TCP/AQM Network Stability Using BBO-Tuned FLC
Full text linkOne of the modern technologies used to improve the performance of various systems, including communications networks and the Internet, is the technology based on biogeography (BBO) that many researchers in the field of automation and control have shed light on. Fuzzy logic is one of the expert systems that has dealt with its use in control systems by many researchers within different applications. The current work has shed light on the mechanism of using The Biogeography Based-Optimization (BBO) technique for adjusting FLC parameters is called (BBO-FLC). The simulation was performed using Matlab program and the researchers adopted the technique as part of the stability of TCP network. The performance of the techniques used in the optimization process can be identified by comparing the results of each case, such as the proposed technique, with other types represented by the traditional control type Proportional–integral–derivative controller (PID). The possibility of using modern and intelligent optimization techniques for the optimal controller is tested using a tuning process for the parameters of the fuzzy type expert controller with the help of the biogeography-based optimization (BBO) technique. The contributions of the research are to verify the possibility of improving the performance by comparing the behavior of the system for the proposed test and simulation cases by obtaining the prescribed level and without exceeding the permissible values