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31,456 Article Results

3D visualization diagnostics for lung cancer detection

10.11591/ijai.v13.i4.pp4630-4641
Rana M. Mahmoud , Mostafa Elgendy , Mohamed Taha
Lung cancer is a leading cause of cancer deaths worldwide with an estimated 2 million new cases and 1·76 million deaths yearly. Early detection can improve survival, and CT scans are a precise imaging technique to diagnose lung cancer. However, analyzing hundreds of 2D CT slices is challenging and can cause false alarms. 3D visualization of lung nodules can aid clinicians in detection and diagnosis. The MobileNet model integrates multi-view and multi-scale nodule features using depthwise separable convolutional layers. These layers split standard convolutions into depthwise and pointwise convolutions to reduce computational cost. Finally, the 3D pulmonary nodular models were created using a ray-casting volume rendering approach. Compared to other state-of-the-art deep neural networks, this factorization enables MobileNet to achieve a much lower computational cost while maintaining a decent degree of accuracy. The proposed approach was tested on an LIDC dataset of 986 nodules. Experiment findings reveal that MobileNet provides exceptional segmentation performance on the LIDC dataset, with an accuracy of 93.3%. The study demonstrates that the MobileNet detects and segments lung nodules somewhat better than other older technologies. As a result, the proposed system proposes an automated 3D lung cancer tumor visualization.
Volume: 13
Issue: 4
Page: 4630-4641
Publish at: 2024-12-01

A study of Tobacco use and mortality by data mining

10.11591/ijece.v14i6.pp6861-6873
Laberiano Andrade Arenas , Inoc Rubio Paucar , Cesar Yactayo-Arias
The use of data mining to address the issue of people who consume tobacco and other harmful substances for their health has led to a significant dependence among smokers, which over time causes illnesses that may result in the addict's death. As a result, the research's goal is to apply a data mining study whose findings showed that the confidence intervals are less than 0.355. However, the lift and conviction in the last three rules are also lower, making it unlikely that these rules will be followed. On the other hand, the knowledge discovery in data bases method was used. It consists of the following stages: data selection, preparation, data mining, and evaluation and interpretation of the results. To that end, comparisons of agile data mining methodologies like crisp-dm, knowledge discovery in data, and Semma are also done. As a result, using specific criteria, dimensions are segmented to allow for the differentiation of these methodologies. As a result, a comparison graph of models such as naive Bayes, decision trees, and rule induction is used. To sum up, it can be said that the rules of association apply to men, the number of admissions, and the cancers that can be brought on by smoking. Also, the percentage of male patients admitted with cancers that can be brought on by smoking Last but not least, the number of admissions and cancers that can be brought on by smoking
Volume: 14
Issue: 6
Page: 6846-6860
Publish at: 2024-12-01

Fractional order PID controller design of a DC-DC multilevel boost converter using the Nelder-Mead optimization

10.11591/ijpeds.v15.i4.pp2409-2421
Ghrissi Tahri , Fatima Tahri , Ali Tahri
This paper aims to present and analyze the fractional order proportional-integral-derivative (FOPID) control technique of the DC-DC two-level boost converter. The state-space averaged (SSA) method is used to build a small-signal converter mathematical model, which is a crucial task for the control design. A FOPID controller based on the Nelder-Mead optimization algorithm and an artificial intelligence strategy based on a fuzzy logic controller (FLC) are both designed for the voltage mode control (VMC) approach and operate on the continuous conduction mode (CCM). Various step changes in the input voltage amplitude and output load are applied to analyze the performance of the proposed control techniques. In addition, detailed simulation results using the MATLAB-Simulink system are extensively discussed.
Volume: 15
Issue: 4
Page: 2409-2421
Publish at: 2024-12-01

A comparative study of machine learning tools for detecting Trojan horse infections in cloud computing environments

10.11591/ijece.v14i6.pp6642-6655
Hasan Kanaker , Monther Tarawneh , Nader Abdel Karim , Adeeb Alsaaidah , Maher Abuhamdeh , Osama Qtaish , Essam Alhroob , Zaid Alhalhouli
Cloud computing offers several advantages, including cost savings and easy access to resources, it is also could be vulnerable to serious security attacks such as cloud Trojan horse infection attacks. To address this issue, machine learning is a promising approach for detecting these threats. Thus, different machine learning tools and models have been employed to detect Trojan horse infection such as Weka and Python Colab. This study aims to compare the performance of Weka and Python Colab, as popular tools for building machine learning models. This study evaluates the recall, accuracy, and F1-score of machine learning models built with Weka and Python Colab and compares their computational resources required employing several machine learning algorithms. The dataset collected and analyzed using dynamic analysis of Trojan horse infection in control lab environment. The findings of this study can help determine the decision about which tool to use to detect Trojan horse infections and provide insights into the strengths and limitations of Weka and Python Colab for building machine-learning models in general.
Volume: 14
Issue: 6
Page: 6642-6655
Publish at: 2024-12-01

Faults-as-address simulation

10.11591/ijra.v13i4.pp452-468
Vladimir Hahanov , Svetlana Chumachenko , Eugenia Litvinova , Ivan Hahanov , Veronika Ponomarova , Hanna Khakhanova , Georgiy Kulak
Fault-as-address-simulation (FAAS) is a simulation mechanism for testing combinations of circuit line faults, represented by the bit addresses of element logical vectors. The XOR relationship between the test set T and the truth table L of the element forms a deductive vector for fault simulation, using truth table addresses or the logic vector bits. Addresses are used in the simulation matrix to mark those n-combinations of input faults detected at the element's output. The columns of the simulation matrix are treated as n-row addresses to generate an element output row via a deductive vector. There is no transport of input faults to the element output, Only the 1-signals written in the non-input row coordinates of the circuit simulation matrix. The simulation matrix is initially filled with 1-signals along the main diagonal. The line faults detected on the test set of circuits are determined by the inverse of lines good values, which have 1-values in the matrix row corresponding to the output circuit element. The deductive vector is obtained by the XOR-relations between the test set and logical vector in three table operations. The advantage of the proposed FAAS mechanism is the predictable complexity of the algorithm and memory consumption for storing data structures when simulating a test set.
Volume: 13
Issue: 4
Page: 452-468
Publish at: 2024-12-01

A systematic assertive wide-band routing using location and potential aware technique

10.11591/ijai.v13.i4.pp3892-3899
Karur Mohammed Saifuddin , Geetha D. Devangavi
Delays occur when packets must be routed over several paths in a wireless sensor network with multiple origins and destinations. There are several causes, delays may occur everywhere, even in a multi-hop wireless network. Due to the broadcast nature of wireless networks, opportunistic routing was able to circumvent these delays. To avoid unnecessary delays, wide-band routing may be used to calculate the smaller path between two nodes. In this case, we address the shortcomings of the standard approach by taking into account the node's power. Path routing as well as the broadcast nature of wireless signals help mitigate the effects of shoddy wireless connections. The results show that the suggested approach outperformed the baseline in both end-to-end latency and packet delivery ratio.
Volume: 13
Issue: 4
Page: 3892-3899
Publish at: 2024-12-01

Experimental results on position and path control of an automated guided vehicle using fixed camera at ceiling and color markers

10.11591/ijra.v13i4.pp391-400
Ramesh Pungle , Atul Andhare , Durva Pungle
This article presents the results of experiments on path planning and control of automated guided vehicles (AGV) using single, fixed ceiling mounted, monocular cameras and colored markers. The camera employed in the system serves as both a sensor and controller. Initially, the working environment is structured using colored markers for given applications. For every new setup, structuring the environment is essential. The image processing algorithm identifies the colored markers and their positions, which are then utilized for path planning and segmentation. The actuation time required to transverse each segment is calculated and then AGV is actuated accordingly. A transformation or inverse mapping matrix (M), predetermined, is employed for calculating world coordinates from given image coordinates. Path planning and AGV control are across various paths, both with and without static obstacles, in real-time applications. The colored marker detection and recognition accuracy for the given setup have been found cent percentage while the AGV reaches the goal point with an error margin of around 3.9% on straight paths, both with and without obstacles.
Volume: 13
Issue: 4
Page: 391-400
Publish at: 2024-12-01

Design and development of knee rehabilitation robot

10.11591/ijra.v13i4.pp423-431
Devashish Sharma , Sahil Ahamed Shaik , Saran Rajagopal , Manju Mohan , Kuppan Chetty Ramanathan , Pavan Kalyan Lingampally
This research presents a comprehensive design and analysis of a knee rehabilitation platform aimed at aiding individuals with knee dysfunction. Dysfunction in the knee joint can lead to an imbalance in gait and posture during activities of daily living (ADLs) such as standing, walking, and running. This study focuses on developing a 2-degree-of-freedom (2-DoF) knee rehabilitation device capable of mimicking linear and angular movements. A slider mechanism-based knee rehabilitation device is developed and simulated alongside various other mechanisms. The proposed mechanism achieves 32.5° of flexion for a linear movement of 0.45 m within 6 seconds, outperforming other mechanisms. To validate simulation results, a 3D-printed model is fabricated, and experimental studies are conducted under no-load conditions, showing close alignment with simulation outcomes with a deviation of ±5%. The device’s key features include portability, compliance, compactness, and enhanced stiffness. Future research will involve conducting pilot studies to further evaluate the practical efficacy and potential enhancements of the proposed knee rehabilitation platform.
Volume: 13
Issue: 4
Page: 423-431
Publish at: 2024-12-01

The future of artificial intelligence-driven robotics: applications and implications

10.11591/ijra.v13i4.pp361-372
Tole Sutikno
Artificial intelligence (AI)-driven robotics is a rapidly evolving field that is transforming various industries, including healthcare, manufacturing, transportation, logistics, security, retail, agri-food, and construction. The integration of artificial intelligence algorithms and machine learning techniques has propelled robotics beyond mere automation, enabling machines to modify, alter, adjust, learn, and interact with the world in ways previously deemed science fiction. The relentless pursuit of creating intelligent robotic systems has led to a symbiotic relationship between human inventiveness and AI, with AI-driven autonomous cars, drones, and robots transforming transportation, healthcare, and exploration. It offers flexibility and learning capabilities, transforming the way machines interact with humans. The integration of AI and robotics marks a transformative era in which machines become companions and cognitive extensions of human capabilities. In the future, we expect AI-driven robotics to bring significant changes to employment and societal well-being. However, the development of AI-driven robotics, which is the integration of AI and robotics, faces numerous challenges, including ethical concerns, legal issues, regulations, societal implications, and job market impacts for the proliferation of intelligent machines. Furthermore, it also presents challenges in terms of technical complexities in its development.
Volume: 13
Issue: 4
Page: 361-372
Publish at: 2024-12-01

A systematic literature review on the impact of participation in sport and physical activities on psychological resilience

10.11591/ijphs.v13i4.24345
Haslinda Husain , Shamsulariffin Samsudin , Ahmad Fauzi Mohd Ayub , Mohamad Firdaus Ahmad , Nur Syazwani Zulaikha S afwan
Participation in sports and physical activity (PA) is often considered a potential contributor to psychological resilience due to its physical, cognitive, and social benefits. This review aims to synthesize an extensive overview of the existing knowledge landscape, shedding light on the mechanisms through which sport and PA may contribute to psychological resilience among students. Guided by the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement review method, we comprehensively searched key databases, including EBSCOhost and Scopus. Note that a total of 353 publications were initially determined. The final finding data concludes that 15 met the selection criteria. The findings were synthesized based on three themes: enhancing positive mental health, reducing negative emotions, and mitigating depression and anxiety. These outcomes highlight the transformative potential of sports and PAs in nurturing a robust psychological foundation among student populations. However, it calls for more robust and standardized research approaches. This is with implications suggesting the integration of sports and PA into comprehensive mental health programs, emphasizing their multifaceted benefits beyond psychological resilience. Tailoring interventions, recognizing the social aspects, and emphasizing the personalized approach can contribute to more effective mental health promotion strategies for students.
Volume: 13
Issue: 4
Page: 1727-1737
Publish at: 2024-12-01

Vision-based approach for human motion detection and smart appliance control

10.11591/ijra.v13i4.pp445-451
Siddharth Swami , Rajesh Singh , Anita Gehlot , Mohammed Ismail Iqbal , Sameer Dev Sharma , Dharmendra Kumar , Sanjeev Kumar Shah
This study focuses on the use of computer vision technology and motion detection sensors to create an intelligent system that recognizes human presence in monitored spaces. The system uses a relay module for automation and control of household appliances while sensing motion detection, operated by an ESP32 microcontroller. This innovative solution addresses two major issues in home automation: reliable human presence recognition and seamless appliance control. The research merges a camera-based vision system with motion sensors, comparing motion and vision-based identification. The ESP32 microcontroller improves motion detection precision and context awareness by integrating motion sensors and computer vision technologies. The integration of a camera module allows real-time analysis and recognition of human presence, reducing false alarms. The relay module also enables automated control of home appliances, synchronizing and feedbacking operations with sensed human presence. The dynamic adaptation of the system improves user convenience and energy efficiency.
Volume: 13
Issue: 4
Page: 445-451
Publish at: 2024-12-01

Study of roundwood sorting on the simulation model of longitudinal chain conveyor

10.11591/ijpeds.v15.i4.pp2068-2079
Nurbanu Sarbassova , Anara Umurzakova , Sergey Kladiev
When sorting roundwood on chain longitudinal conveyors of considerable length, there is an error in moving the selected roundwood assortment to the place of discharge into the accumulator, which increases in proportion to the length of the sorting line. Currently, conveyors of limited length are used. The accumulators are located on both sides of the conveyor. This reduces the transportation error. Sometimes the sorting yard does not allow placing a short conveyor on it or there is a considerable amount of assortments. In this case, it is necessary to use a conveyor of considerable length. The paper proposes a new method of sorting roundwood on longitudinal chain conveyors of considerable length. This reduces the tracking error of the assortment movement. Angular displacement sensors of the leading and driven sprockets of the closed plate chain of the sorting conveyor are used as sensors of angular displacements. The shafts of both sensors are mechanically connected to the shafts of the driving and driven sprockets, respectively. The results of simulation modeling of the sorting control system with complex mechanical system confirmed the possibility of practical application of the proposed method to reduce the tracking error of the sorted cargo. The results of the study confirm the possibility of practical application of the device for operation on longitudinal conveyors.
Volume: 15
Issue: 4
Page: 2068-2079
Publish at: 2024-12-01

Systematic review of artificial intelligence with near-infrared in blueberries

10.11591/ijai.v13.i4.pp3761-3771
Liset Cayhualla Amaro , Sebastian Rau Reyes , María Acuña Meléndez , Christian Ovalle
The fruit quality has a direct impact on how the fruit looks and how tasty the fruit is. The correct use of tools to determine fruit quality is essential to offer the best product for the final consumer. This study has used the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. The study objective was elaborate a systematic literature review (SLR) about research of the application of techniques based on artificial intelligence to analyze indicators obtained by near infrared spectroscopy (NIRS) and chemometrics to determine the quality of fruits, including blueberries. The most frequently addressed indicator is the soluble solids concentration (SSC) which was used in several studies with techniques such as support vector machines (SVM) and convolutional neural networks (CNN). According to the results obtained, it is possible to use these techniques to predict blueberry quality indicators. There was an acceptable performance and high accuracy of these models. However, future research could cover other techniques and help to provide better quality control of products in food industries.
Volume: 13
Issue: 4
Page: 3761-3771
Publish at: 2024-12-01

Analysis of single layer artificial neural network neuromorphic hardware chip

10.11591/ijra.v13i4.pp495-505
Aruna Pant , Adesh Kumar , Piyush Kuchhal
The neuromorphic architectures are hardware network systems designed with neural functions. Neural networks seen in biology serve as an inspiration for network systems. A synapse connects every node or neuron in an artificial neural network (ANN) to every other node. As in biological brains, the amplitude of the linking between nodes referred to as synaptic weights will regulate the connection. In contrast to conventional design, ANN uses many highly organized dealing pieces that work together to solve real-world issues. The design of the neuromorphic hardware chip is discussed in the paper. The target device used is a Virtex-5 Field Programmable Gate Array (FPGA) and the simulation is taken on Xilinx ModelSim software. This chip is designed for 20 neuron inputs, each of the neuron inputs is 8-bit. Each 20-neuron input is multiplied by 20 input weights and each weight is 8-bit so when these 20 input weights are multiplied by 20 neuron inputs in the multiplier it gives 16-bit output. A control logic is used in this neuromorphic hardware chip design which is used to feed multiplier output to each input of the hidden layer. The system-level outcome of the hidden layer is then given to the multiplexer which has 20 inputs and one single output. The multiplexer is used to select any of the 20 outputs of the control logic. Finally, to gain an understanding of the performance of this neuromorphic hardware chip, we have computed the hardware utilization parameters. These parameters include slices, input/output blocks (IOBs), registers used, memory, and the overall propagation delay used by the hardware chip.
Volume: 13
Issue: 4
Page: 495-505
Publish at: 2024-12-01

A neural machine translation system for Kreol Repiblik Moris and English

10.11591/ijai.v13.i4.pp4976-4987
Sameerchand Pudaruth , Sheeba Armoogum , Nirmal Kumar Betchoo , Aneerav Sukhoo , Vandanah Gooria , Abdallah Peerally , Mohammad Zafar Khodabocus
Although Google Translate is a widely used machine translation service that supports 133 languages, it does not incorporate support for the Kreol Repiblik Moris (KRM) language. Addressing this limitation, the current research focuses on enhancing the accuracy and fluency of machine translation between KRM and English through natural language processing and deep neural machine translation techniques. In this study, a machine translation system using a transformer model trained with a dataset of 50,000 parallel corpora has been developed. The model was evaluated using manual translations and the bilingual evaluation understudy (BLEU) score. A score of 31.46 for translating from KRM to English and 28.15 for translating from English to KRM was achieved. To our knowledge, these are the highest BLEU scores for translation between these two languages. This is due to utilising the largest dataset and extensive atomic words from the KRM dictionary. This successful interdisciplinary funded project led to the setting up of a free online translation service and a smartphone app for Mauritian citizens and tourists.
Volume: 13
Issue: 4
Page: 4976-4987
Publish at: 2024-12-01
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