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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

Malay phoneme-based subword news headline generator for low-resource language

10.11591/ijai.v13.i4.pp4965-4975
Yeong Tsann Phua , Kwang Hooi Yew , Mohd Fadzil Hassan , Matthew Teow Yok Wooi
The booming of technology has significantly increased the amount of news articles for readers. The headline of news plays an essential role in attracting readers. Traditionally, crafting the news headline is a manual task at the news desk. The motivation of this paper is to address the issues faced in low resource languages, such as the Malay language. The main contribution of this paper is a new hybrid model based on extractive- and abstractive-based text summarization with the integration of a geographical linguistics model; a Malay phoneme-based subword embedding has been developed to solve the complex morphological issue in the Malay language-based computational linguistic applications. The experiment involves various sequence-to sequence (seq2seq) models to generate the Malay news headlines. Besides that, the out-of-vocabulary (OOV) is assessed in the models. From the experiment, the proposed hybrid text summarization model shows significant improvement over the baseline models above 11.00 in ROUGE-1, 4.00 ROUGE-2, and 11.00 in ROUGE-L. The proposed model can reduce the OOV rate to below 15%.
Volume: 13
Issue: 4
Page: 4965-4975
Publish at: 2024-12-01

Federated inception-multi-head attention models for cyber-attacks detection

10.11591/ijai.v13.i4.pp4778-4794
Imad Tareq AL-Halboosi , Bassant Mohamed Elbagoury , Salsabil Amin El-Regaily , El-Sayed M. El-Horbaty
With the proliferation of internet of things (IoT) devices, ensuring the security of these interconnected systems has become a critical concern. Cyberattacks targeting IoT devices pose significant threats to individuals and organizations due to the generation of vast amounts of data across many connected devices, which traditional centralized methods cannot solve. Federated learning (FL) could be a promising solution to mitigate privacy concerns associated with centralized approaches and address cybersecurity concerns. This paper uses FL and deep learning (DL) approaches to cybersecurity in IoT applications. The goal of cyber security is achieved by forming a federation of acquired and shared models at the head of the various participants. We use inception time and multi-head attention (CNN) algorithm based on FL to detect cyber-attacks and avoid data privacy leaks under two distribution modes, namely IID and Non-IID. In contrast, the FedAvg and FedMA algorithms aggregate local model updates. A global model is produced after several communication rounds between the IoT devices and the model parameter server. Cyber threats are simulated using edge-IIoT datasets. Experiment results show that the federated inception model's best global accuracy was 93, 91%, and 93, 49% using multi-head attention.
Volume: 13
Issue: 4
Page: 4778-4794
Publish at: 2024-12-01

Impact of start-stop systems on motorcycle fuel savings in urban traffic

10.11591/ijece.v14i6.pp6258-6264
Kevin Murga-Garcia , Rodrigo Chacaltana-Silva , Ernesto Paiva-Peredo
The start/stop (S/S) system implemented in motorcycles aims primarily at fuel savings. This study was conducted to assess the effectiveness of this system in conditions of heavy traffic and traffic lights in Lima, using a virtual channel identifier (VCI) and a technical schedule. The detailed analysis covered critical aspects of the S/S system, the description of the route taken, and its segmentation to understand the number of stops and mileage. Speed limits, schedules, and measurement equipment were established, including the MICODUS-ORBD2 device and the VCI-Hero. The study included tests conducted with and without the MICODUS-ORBD2 device, recording times, distances, and fuel consumption. Data were collected with the S/S activated and deactivated, concluding the system achieves a 10.1% fuel saving. This finding provides valuable insights into understanding the system's effectiveness in actual traffic conditions and emphasizes the importance of maintaining key vehicle components to optimize S/S performance.
Volume: 14
Issue: 6
Page: 6258-6264
Publish at: 2024-12-01

Aspect-based sentiment analysis: natural language understanding for implicit review

10.11591/ijece.v14i6.pp6711-6722
Suhariyanto Suhariyanto , Riyanarto Sarno , Chastine Fatichah , Rachmad Abdullah
The different types of implicit reviews should be well understood so that the developed extraction technique can solve all problems in implicit reviews and produce precise terms of aspects and opinions. We propose an aspect-based sentiment analysis (ABSA) method with natural language understanding for implicit reviews based on sentence and word structure. We built a text extraction method using a machine learning algorithm rule with a deep understanding of different types of sentences and words. Furthermore, the aspect category of each review is determined by measuring the word similarity between the aspect terms contained in each review and aspect keywords extracted from Wikipedia. Bidirectional encoder representations from transformers (BERT) embedding and semantic similarity are used to measure the word similarity value. Moreover, the proposed ABSA method uses BERT, a hybrid lexicon, and manual weighting of opinion terms. The purpose of the hybrid lexicon and the manual weighting of opinion terms is to update the existing lexicon and solve the problem of weighting words and phrases of opinion terms. The evaluation results were very good, with average F1-scores of 93.84% for aspect categorization and 92.42% for ABSA.
Volume: 14
Issue: 6
Page: 6711-6722
Publish at: 2024-12-01

Risk factors and trends analysis of unwanted pregnancy in Indonesia

10.11591/ijphs.v13i4.23702
Supriyadi supriyadi , Tris Eryando , Purnawan Junadi
Unwanted pregnancy has serious and adverse health, social, and economic impacts, as well as the risk of maternal and child mortality. In Indonesia, unwanted pregnancy is one of the most troubling public health problems and a major reproductive health problem. The sample consisted of 2002 (12,612), 2007 (14,471), 2012 (14,212), and 2017 (8,838), so the total sample was 50,233 couples of childbearing age by random sample method. This study was analyzed using logistic regression analysis. Unintended pregnancies were associated with maternal age, age at first marriage, number of children, maternal education, contraceptive use, contraceptive failure, residence, and the interval between pregnancies. Unwanted pregnancies at the mother's vulnerable age have increased over the past 10 years and decreased after entering the last 5 years, tend to be stable and constant at the age of first marriage. About one and a half women in Indonesia encounter unwanted pregnancies. Unwanted pregnancies fluctuate over 20 years. Women, especially the most vulnerable should be empowered to avoid unwanted pregnancies for their awareness and independence. Programs to expand access and quality of information and counseling centers, access and quality of women's health services, and specific reproductive health services as needed.
Volume: 13
Issue: 4
Page: 1864-1871
Publish at: 2024-12-01

Network intrusion detection in big datasets using Spark environment and incremental learning

10.11591/ijai.v13.i4.pp4414-4421
Abdelwahed Elmoutaoukkil , Mohamed Hamlich , Amine Khatib , Marouane Chriss
Internet of things (IoT) systems have experienced significant growth in data traffic, resulting in security and real-time processing issues. Intrusion detection systems (IDS) are currently an indispensable tool for self-protection against various attacks. However, IoT systems face serious challenges due to the functional diversity of attacks, resulting in detection methods with machine learning (ML) and limited static models generated by the linear discriminant analysis (LDA) algorithm. The process entails adjusting the model parameters in real time as new data arrives. This paper proposes a new method of an IDS based on the LDA algorithm with the incremental model. The model framework is trained and tested on the IoT intrusion dataset (UNSW-NB15) using the streaming linear discriminant analysis (SLDA) ML algorithm. Our approach increased model accuracy after each training, resulting in continuous model improvement. The comparison reveals that our dynamic model becomes more accurate after each batch and can detect new types of attacks.
Volume: 13
Issue: 4
Page: 4414-4421
Publish at: 2024-12-01

A machine learning based framework for breast cancer prediction using biomarkers

10.11591/ijai.v13.i4.pp4344-4351
Apurva Vashist , Anil Kumar Sagar , Anjali Goyal
Breast cancer is the most frequent cancer in women and the second-leading cause of cancer-related deaths globally. The main problems in managing breast cancer are high heterogeneity and the formation of therapeutic resistance. White blood cells, omics and large Wisconsin diagnostic breast cancer datasets present the three-decade genomic revolution and advance the understanding of cellular function. The precision of cancer diagnosis has also increased over the past decades. High throughput sequencing, screening, and artificial intelligence technologies have significantly improved and increased the methodologies used for diagnosis, prognosis, and therapy. This paper follows several phases of breast cancer, studies datasets and evaluate many algorithms of machine learning (ML) used for analysis and feature selection i.e. k-means, similarity correlation, genetic algorithm, and principal component analysis, have been used to recognize the subset of proteins with the highest significance for breast cancer prediction by using different biomarkers. The best correlation, as determined by Pearson correlation, between copy number and protein is 0.014, and the accuracy achieved by the genetic algorithm is 93.5% using multi-omics datasets.
Volume: 13
Issue: 4
Page: 4344-4351
Publish at: 2024-12-01

Spectral splitting of speech signal using time varying recursive filters for binaural hearing aids

10.11591/ijai.v13.i4.pp4998-5004
Aparna Chilakawad , Pandurangarao N. Kulkarni
Speech perception in noisy environments is reduced in human with sensorineural hearing loss (SNHL) due to masking. Moderate SNHL cannot be cured medically hence masking effects should be reduced to enhance speech perception. To reduce masking, processing delay and hardware complexity the present paper is proposed a scheme to partition the voice signal into two signals which are complementary to each other by using the filter-bank summation method (FBSM) with a set of time-varying recursive band pass filters. Performance of the filter is evaluated with following measures: perceptual evaluation of speech quality (PESQ), mean opinion score (MOS) for speech quality and modified rhyme test (MRT) for speech intelligibility. The test signals used for the evaluation of quality are a syllable and a word and for the evaluation of intelligibility 300 monosyllabic words are used. The results demonstrated an increase in the quality and intelligibility of processed speech in a noisy environment. As a result, there is an enhancement in perception of processed speech in a noisy environment.
Volume: 13
Issue: 4
Page: 4998-5004
Publish at: 2024-12-01

The use of augmented reality in assessing and training children with attention deficit hyperactivity disorder

10.11591/ijai.v13.i4.pp4045-4053
Jesla Joseph , Vinay M.
Attention deficit hyperactivity disorder (ADHD) is a serious issue that must be addressed in the modern world. Treatment for ADHD is challenging because it is costly, has adverse effects, might not be successful, and is not considered an emergency. The reason that ADHD is hard to manage is because it causes people-especially children-to make impulsive decisions that hinder their ability to succeed in school, the workplace, and other areas of life. As an alternative approach, neurofeedback therapy or play therapy, which relies on real-time feedback of an individual's brainwave activity typically collected through electroencephalogram (EEG), has demonstrated promising outcomes in the treatment of mental disorders and enhancing cognitive capabilities. On the other hand, prolonged exposure to repetitive feedback might result in lower engagement since people may become disinterested in the process and find it difficult to continue participating. An extensive assessment on the use of augmented reality (AR) in the context of pediatric ADHD has been carried out, with an emphasis on the benefits of creating games specifically for kids with ADHD. By using AR technology in a group of children, the goal of this study was to investigate the basic characteristics of AR systems that aid in the identification and treatment of ADHD in children.
Volume: 13
Issue: 4
Page: 4045-4053
Publish at: 2024-12-01

Ethnocultural wisdom and development of e-comic

10.11591/ijere.v13i6.29176
Ida Ermiana , Ni Nyoman Parwati , I Gusti Putu Sudiarta , I Gde Wawan Sudatha
Cultivating local cultural values by adapting to technological advances, found in digital comic learning media oriented towards local Sasak wisdom, provides ease of learning and communicative language and is easy to access but still needs to be improved. The research aims to develop digital comic media that is feasible, practical and effective, oriented towards Sasak local wisdom (e-kodisa). The development stage uses the stages of analysis, design, development, implementation and evaluation (ADDIE). The research results show that digital comic media oriented towards Sasak local wisdom from material experts in category 1 is feasible, and material experts in category 2 are possible. The assessment of digital comic media from Media expert 1 is in the attainable category, and media expert 2 is very doable. The evaluation of learning design experts and language experts is in the very decent category. Trials on students show that digital comic media is practical and can be used in learning. The results of the effectiveness test showed sig. (2-tailed)<0.05 Ho is rejected, and Ha is accepted, meaning a significant difference exists between the pretest and posttest averages. So, developing digital comic media oriented towards Sasak local wisdom is feasible, practical and effective.
Volume: 13
Issue: 6
Page: 3618-3627
Publish at: 2024-12-01

Information system architecture for healthcare company based on TOGAF

10.11591/ijaas.v13.i4.pp806-813
Vania Christy , Johanes Fernandes Andry , Ahya Radiatul Kamila , Francka Sakti Lee
In 2020 COVID-19 cases have entered Indonesia causing public health problems and millions of deaths. To prevent transmission of COVID-19, an air purifier is needed whose function is to remove small droplets that can carry the virus. One of them is a medical device company located in Jakarta. The purpose of this research is to produce a design that can improve business processes in the health sector and achieve company goals. The current business process is not very optimal because it is still done conventionally and the existing system has not been integrated with other divisions. To achieve business goals, it is necessary to integrate business processes with information technology (IT) and technology development that will be proposed based on the design of information system architecture that will produce a blueprint and assisted by the open group architecture framework (TOGAF) framework which is very helpful in the process of analyzing company needs. In this research, data collection through interviews with directors and direct observation of health service companies. The results of this study are recommendations given to help health.
Volume: 13
Issue: 4
Page: 806-813
Publish at: 2024-12-01

Correlation of physical activity with cognition and mental health in medical students

10.11591/ijphs.v13i4.23150
Yhusi Karina Riskawati , Faresa Aulia Rastramadhani , Henny Mulyani , Ridha Saniyyah Rindradi , Muhammad Isa Mahendra , Christyaji Indradmojo , Aswaty Nur
Low physical activity increases the number of non-communicable diseases. This phenomenon occurs in medical students. This study aimed to determine the correlation between sedentary behavior and physical activity on cognition, mental health, cortisol, and brain derived neurotropic factor (BDNF). Using a cross-sectional design of observational study, eighty-six medical students were involved in this study. The subjects were interviewed to assess cognitive function and depression levels. Saliva was taken to measure cortisol and BDNF level. Bivariate analysis was performed using the Spearman test. Depression is the only variable that correlates significantly with habitual physical activity (p=0.025, r=-0.214). Sedentary behavior has a weak correlation with cognitive failure, anxiety, and depression ([p=0.046, r=0.216]; [p=0.039, r=0.223]; [p=0.011, r=0.273]). The results found that high physical activity improves mental health and cognition. This study suggested that physical activity can alleviate symptoms of depression and anxiety among medical students.
Volume: 13
Issue: 4
Page: 1617-1625
Publish at: 2024-12-01

Eligibility rate of applicant’s LinkedIn account: a naïve bayes classification and visualization

10.11591/ijai.v13.i4.pp4334-4343
Khyrina Airin Fariza Abu Samah , Nurul Athirah Ahmad , Anis Amilah Shari , Hana Fakhira Almarzuki , Zuhri Arafah , Lala Septem Riza , Amir Haikal Abdul Halim
In the digital era, social media platforms like LinkedIn have become famous for recruitment, and recruiters widely use them to find potential employees. The recruitment process is crucial in organizations, as it involves selecting qualified applicants from a diverse pool. However, the screening process and manual recruitment process entail significant time, high costs, and potential bias. Consequently, it may cause recruiting unqualified applicants and may affect the organizations. Thus, this study aims to classify and generate a list of potential job applicants by analyzing seven attributes of their LinkedIn accounts: title, location, skills, education, language, certification, and years of experience. Data are collected from LinkedIn profiles and then undergo data pre-processing. The naive Bayes (NB) algorithm is implemented as the classification algorithm and sets the classification as “eligible” or “ineligible”. The NB model achieved an accuracy testing of 89.8%, indicating good performance in classifying potential job applicants. At the same time, we measure the similarity cosine score to set the mean of the eligibility. The classification results are visualized for the suitable applicants in descending rank, allowing users to choose the applicants’ classification status efficiently. For the system usability, we managed to get 90% from the recruitment expert.
Volume: 13
Issue: 4
Page: 4334-4343
Publish at: 2024-12-01

Parallel numerical simulation of the 2D acoustic wave equation

10.11591/ijece.v14i6.pp6519-6525
Arshyn Altybay , Dauren Darkenbayev , Nurbapa Mekebayev
Mathematical simulation has significantly broadened with the advancement of parallel computing, particularly in its capacity to comprehend physical phenomena across extensive temporal and spatial dimensions. High-performance parallel computing finds extensive application across diverse domains of technology and science, including the realm of acoustics. This research investigates the numerical modeling and parallel processing of the two-dimensional acoustic wave equation in both uniform and non-uniform media. Our approach employs implicit difference schemes, with the cyclic reduction algorithm used to obtain an approximate solution. We then adapt the sequential algorithm for parallel execution on a graphics processing unit (GPU). Ultimately, our findings demonstrate the effectiveness of the parallel approach in yielding favorable results.
Volume: 14
Issue: 6
Page: 6519-6525
Publish at: 2024-12-01
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