Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

31,456 Article Results

Application of data mining for diagnosis of ENT diseases using the Naïve Bayes method with genetic algorithm feature selection

10.11591/ijeecs.v37.i1.pp398-405
Linda Perdana Wanti , Nur Wachid Adi Prasetya , Ihza Awaludin , Muhammad Bintang Aditya Saputra , Syamaidzar Nadifa Furi , Dimas Maulana Dwi Kumara
Ear, nose, and throat (ENT) disease is a disorder that occurs in the eustachian tube in one of the organs, be it the ear, nose, or throat. Early signs of ENT disease include sore throat, painful swallowing, swollen and red tonsils, runny nose, nosebleeds, blocked nose, discharge from the ears, and others. To determine the diagnosis, it is necessary to carry out a physical examination of the ears, nose, and throat as recommended by an expert, namely an ENT doctor. The research carried out was implementing data mining for the diagnosis of ENT diseases using the Naïve Bayes (NB) method. This method was chosen because it can increase the accuracy, efficiency, and accessibility of health services and is also easy to understand and apply to classify ENT disease symptom data. The NB method was used to build an ENT diagnosis classification model and the model performance was evaluated using accuracy, precision, and recall metrics. To increase the accuracy of the NB algorithm predictions, feature selection using a genetic algorithm can be used. Genetic algorithms can help select the most relevant and significant features, improving the accuracy of NB models by eliminating irrelevant or noisy features. By applying this method, predictions for ENT diseases can be produced with an accuracy of 95.67%.
Volume: 37
Issue: 1
Page: 398-405
Publish at: 2025-01-01

Spread of harmful substances in the atmosphere of industrial cities of Kazakhstan: modeling and data refinement

10.11591/ijeecs.v37.i1.pp636-647
Nurlan Temirbekov , Dinara Tamabay , Moldir Tanashova
In Kazakhstan, air pollution in industrial cities poses a significant challenge that requires urgent attention. This study investigates the dispersion of harmful pollutants in the air across nine prominent industrial cities in Kazakhstan. The research involves modeling the emissions from major pollution sources for each city, which provides a comprehensive view of how these substances spread through the atmosphere. The study also examines the distribution patterns of these pollutants to gauge their concentration levels in each urban area. Additionally, it addresses the inverse problem of data assimilation from automated monitoring stations (AMS), aiming to refine the information on pollution sources. By utilizing the conjugate equations method, the study successfully converged to an accurate solution. Detailed visualizations for Almaty, Ust-Kamenogorsk, and Pavlodar illustrate the pollution dynamics and pinpoint the most affected regions. These findings are crucial for formulating strategies to mitigate the adverse effects of industrial emissions on both the environment and public health.
Volume: 37
Issue: 1
Page: 636-647
Publish at: 2025-01-01

Enhancing hypertension prediction: a hybrid machine learning optimization approach

10.11591/ijeecs.v37.i1.pp347-355
Abd Allah Aouragh , Mohamed Bahaj , Fouad Toufik
Early identification of hypertension is crucial to prevent its serious complications, which can lead to devastating health effects by threatening lifestyle quality and significantly increasing premature mortality. This study aims to evaluate the effectiveness of machine learning techniques in predicting the presence of hypertension from an unbalanced dataset consisting of 4,363 records and 35 features. To balance the dataset, we employed the synthetic minority over-sampling technique (SMOTE) algorithm. In addition, to select the most relevant features, we used ant colony optimization. Next, we applied various algorithms, including logistic regression (LR), K-nearest neighbors (KNNs), support vector machine (SVM), extra trees (ETs), and AdaBoost (AB). We also evaluated the optimization of hyperparameters using two methods: Bayesian optimization (BO) and particle swarm optimization (PSO). The results reveal that the combination of AB with BO demonstrated superior performance, with an accuracy of 97.60%, a recall of 98.93%, and a precision of 98.59%. This research emphasizes the potential of machine learning techniques for anticipating hypertension and highlights the importance of optimization techniques in improving predictive models’ performance.
Volume: 37
Issue: 1
Page: 347-355
Publish at: 2025-01-01

Attention based English to Indo-Aryan and Dravidian language translation using sparsely factored NMT

10.11591/ijeecs.v37.i1.pp250-256
Ritesh Kumar Dwivedi , Parma Nand , Om Pal
Neural machine translation (NMT) is a sophisticated technique that employs a large, singular neural network to learn and execute automatic translation tasks. Unlike statistical machine translation systems, NMT handles the entire translation process in an end-to-end manner, removing the need for additional components. This approach has shown significant promise in translation quality and has become the prevalent method. In this study, we apply sparsely factored NMT to English and several Indo-Aryan (Hindi, Bengali) and Dravidian (Tamil, Malayalam) language pairs. Specifically, we develop the machine translation system using an attention-based mechanism. A significant problem with traditional transformers is the huge memory requirement. Therefore, a sparsely factored NMT (SFNMT) is used to reduce the memory requirement but also improves the training time, thereby, reducing the computing time. In this paper, take inspiration from Vaswani transformer and modify it to get the best results. The system’s performance was evaluated using the BLEU metric. The proposed model indtrl achieves a BLUE score of 32.13 (en→hi), 29.31 (en→be), 31.21 (en→ta), 21.12 (en→ml) and 32.67 (en→hi), 29.38 (en→be), 31.75 (en→ta), 21.17 (en→ml) without backtranslation and with backtranslation. To evaluate the performance of the system, we compared the results with those of existing systems. The developed system demonstrated a marginally higher BLEU score than both AnglaMT and Google translate.
Volume: 37
Issue: 1
Page: 250-256
Publish at: 2025-01-01

Job matching analysis by latent semantic indexing enhanced on multilingual word meanings

10.11591/ijeecs.v37.i1.pp434-442
Sukri Sukri , Noor Azah Samsudin , Ezak Fadzrin , Shamsul Kamal Ahmad Khalid , Liza Trisnawati
Job matching is a hiring process that involves a thorough understanding of the context and meaning of words in different languages. The updated and expanded latent semantic indexing (LSI) Framework seeks to improve the precision and relevance of job matching analysis of word meanings in multi-languages. Because they only compare related terms, conventional LSIs are often insufficient to address the complexity of context in job matching. Extending the LSI approach can improve the vector representation of words and help you understand the context and semantic relationships in the text. Improved LSI analyzes context more precisely by using word vector representation. Improved LSI focuses on understanding semantic relationships between words in many languages to produce more accurate and relevant job matches. This paper describes the steps involved in improving LSI, such as data collection, pre-processing, linguistic feature extraction, LSI model training, and evaluation of matching results. The results show that the examined classification model has much better performance in terms of word classification. Conventional LSI has an average prediction value of 79%, once the enhanced LSI can accurately predict about 84% of the entire word, it has a reasonable capacity to recognize the actual words in a natural context.
Volume: 37
Issue: 1
Page: 434-442
Publish at: 2025-01-01

Elevating intelligent voice assistant chatbots with natural language processing, and OpenAI technologies

10.11591/ijeecs.v37.i1.pp507-517
Nilesh B. Korade , Mahendra B. Salunke , Amol A. Bhosle , Gayatri G. Asalkar , Bechoo Lal , Prashant B. Kumbharkar
Businesses can offer support to customers outside of usual business hours and across time zones by employing chatbots, which can provide round-the-clock support. Chatbots can react to user inquiries quickly, reducing wait times and improving customer satisfaction. It becomes challenging for the chatbot to differentiate between two queries that users pose that carry the same meaning, making it harder for it to understand and react appropriately. The aim of this research is to develop a chatbot capable of understanding the semantic meaning of questions as well as recognizing various speech patterns, accents, and dialects to provide accurate responses. In this research, we have implemented a voice-enabled chatbot system where users can verbally pose questions, and the chatbot provides responses through voice assistance. The architecture incorporates several key components: a question-answer database, OpenAI embedding for semantic representation, and OpenAI text-to-speech (TTS) and speech-to-text (STT) for audio-to-text and text-to-audio conversion, respectively. Specifically, OpenAI embedding is utilized to encode questions and responses into vector representations, enabling efficient similarity calculations. Additionally, extreme gradient boosting (XGBoost) is trained on OpenAI embeddings to identify similarities between user queries and questions within the dataset. This framework allows for seamless interaction between users and the chatbot, leveraging state-of-the-art technologies in natural language processing (NLP) and speech recognition. The outcome demonstrates that the XGBoost model delivers excellent outcomes when it is trained on OpenAI embedding and tuned with the particle swarm optimizer (PSO). The OpenAI-generated embedding has good potential for capturing sentence similarity and provides excellent information for models trained on it.
Volume: 37
Issue: 1
Page: 507-517
Publish at: 2025-01-01

Enhancing malware detection through self-union feature selection using gray wolf optimizer

10.11591/ijeecs.v37.i1.pp197-205
Mosleh M. Abualhaj , Qusai Y. Shambour , Ahmad Adel Abu-Shareha , Sumaya N. Al-Khatib , Amal Amer
This research explores the impact of malware on the digital world and presents an innovative system to detect and classify malware instances. The suggested system combines a random forest (RF) classifier and gray wolf optimizer (GWO) to identify and detect malware effectively. Therefore, the suggested system is called RFGWO-Mal. The RFGWO-Mal system employs the GWO for feature selection in binary and multiclass classification scenarios. Then, the RFGWO-Mal system uses a novel self-union feature selection approach, combining features from different subsets of binary and multiclass classification extracted using the GWO optimizer. The RF classifier is then applied for classifying malware and benign data. The comprehensive Obfuscated-MalMem2022 dataset was utilized to evaluate the suggested RFGWO-Mal system, which has been implanted using Python. The suggested RFGWO-Mal system achieves significantly improved results using the novel self-union feature selection approach. Specifically, the RFGWO-Mal system achieves an outstanding accuracy of 99.95% in binary classification and maintains a high accuracy of 86.57% with multiclass classification. The findings underscore the achievement of a self-union feature selection approach in enhancing the performance of malware detection systems, providing a valuable contribution to cybersecurity.
Volume: 37
Issue: 1
Page: 197-205
Publish at: 2025-01-01

Comparative analysis of different types of pulse width modulation techniques for multilevel inverters

10.11591/ijeecs.v37.i1.pp680-688
Palanisamy R , M. Nivethitha Devi , Manikandan T. R , K. Mekala Devi , Rashima Mahajan , Selvabharathi D , Selvakumar K
Multilevel inverters have gained significant attention in recent years due to their ability to achieve higher voltage and lower harmonic distortion compared to conventional two-level inverters. Pulse width modulation (PWM) techniques play a crucial role in controlling multilevel inverters by generating the required switching signals for their power electronic devices. This paper presents a comprehensive comparative analysis of various PWM techniques employed in multilevel inverters, including sinusoidal pulse width modulation (SPWM), space vector pulse width modulation (SVPWM), carrier-based pulse width modulation (CBPWM), and selective harmonic elimination (SHEPWM). Each PWM technique's advantages, limitations, and suitability for different multilevel inverter topologies are discussed. Furthermore, recent advancements and hybrid PWM techniques are also examined to explore potential improvements in performance and efficiency. This paper aims to provide researchers, engineers, and practitioners with valuable insights into selecting the most appropriate PWM technique for their specific multilevel inverter applications, considering factors such as performance requirements, cost constraints, and ease of implementation.
Volume: 37
Issue: 1
Page: 680-688
Publish at: 2025-01-01

Enhancing energy efficiency and reliability in wireless sensor networks using BioGAT optimization

10.11591/ijeecs.v37.i1.pp601-612
D. K. Shareef , Veeramreddy Jyothsna
The BioGAT model, as proposed, presents a novel methodology for enhancing the efficiency of wireless sensor networks (WSNs), which are essential elements of contemporary communication and sensing systems. For real-time monitoring and data analysis, WSNs are comprised of autonomous sensor nodes that are outfitted with processing, wireless communication, and sensing capabilities. These nodes are deployed in a variety of environments. By means of an advanced optimization model, this work aims to address critical challenges in WSNs, specifically in the areas of node placement, energy efficiency, and network reliability. By utilizing biogeography-based optimization (BBO) and graph attention networks (GAT), the BioGAT model endeavors to dynamically adapt to network changes while achieving a balance between efficient coverage and energy consumption. Cluster heads (CHs), which are essential for the aggregation of data, have a significant impact on improvements in energy efficiency and the longevity of networks. By means of comprehensive simulations and evaluation, this study presents exceptional outcomes. The BioGAT model outperforms prior approaches by attaining a 95% packet delivery ratio and an enhanced throughput. In addition, the model effectively decreases mean energy consumption, underscoring its capacity to improve the sustainability and dependability of networks in a variety of WSN applications.
Volume: 37
Issue: 1
Page: 601-612
Publish at: 2025-01-01

Random forest algorithm with hill climbing algorithm to improve intrusion detection at endpoint and network

10.11591/ijeecs.v37.i1.pp134-142
Satheesh Kumar Sekar , Palaniraj Rajidurai Parvathy , Latika Pinjarkar , Raman Latha , Mani Sathish , Munnangi Koti Reddy , Subbiah Murugan
Cloud computing is a framework that enables end users to connect highly effective services and applications over the internet effortlessly. In the world of cloud computing, it is a critical problem to deliver services that are both safe and dependable. The best way to lessen the damage caused by entry into this environment is one of the primary security concerns. The fundamental advantage of a cooperative approach to intrusion detection system (IDS) is a superior vision of an action of network attack. This paper proposes a random forest (RF) algorithm with a hill-climbing algorithm (RFHC) to improve intrusion detection at the endpoint and network. Initially, it is used for feature selection, and the next process is to separate the intrusions detection. The feature selection is maintained by the hill climbing (HC) algorithm that chooses the best features. Then, we utilize the RF algorithm to separate the intrusion efficiently. The experimental results depict that the RFHC mechanism reached more acceptable results regarding recall, precision, and accuracy than a baseline mechanism. Moreover, it minimizes the miss detection ratio and enhances the intrusion detection ratio.
Volume: 37
Issue: 1
Page: 134-142
Publish at: 2025-01-01

A new intensity-modulated radiation therapy with deep learning heart rate prediction framework for smart health monitoring

10.11591/ijeecs.v37.i1.pp300-314
Saravanan Madderi Sivalingam , Syed Thisin
This research paper monitors the patient’s health using sensor data, cloud, and big data Hadoop tools and used to predict heart attack and related results were discussed in detail. The integration of big data, and wearable sensors in pervasive computing has significantly enhanced healthcare services. This proposal focuses on developing an advanced healthcare monitoring system tailored for tracking the activities of elderly individuals. The wearable sensors are placed on humans at a right angle, left arm, right arm, and chest to collect the data. The large data are split into smaller segments using the map and reduce process of big data Hadoop tools. The intensity-modulated radiation therapy (IMRT) approach is used for the mapping phase and deep convolutional neural network (DCNN), deep belief network (DBN), and long short-term memory (LSTM) and proposed deep learning heart rate prediction (DLHRP) algorithms are used for the combiner/reduce phase. The reduction process combines similar segments of data to predict identical classes to predict the severity of human conditions. The proposed IMRT-DLHRP system has improved performance of 96.34% accuracy compared with 84.25%, 89.47%, and 91.58% compared to DCNN, DBN, and LSTM respectively, therefore proposed framework has significant improvement over existing approaches.
Volume: 37
Issue: 1
Page: 300-314
Publish at: 2025-01-01

MODIS-NDVI and wheat yield patterns and predictions in Taounate, Morocco

10.11591/ijeecs.v37.i1.pp648-659
Sara Bouskour , Mohamed Hicham Zaggaf , Lhoussain Bahatti , Soumaya Zayrit
This study is devoted to the use of varied analytical methods to elucidate the complex relationship between normalized difference vegetation index (NDVI) and wheat production in Taounate, Morocco based on MODIS Satellite data. Linear regression (LR), with a coefficient of determination (R²) of 0.93, provided a solid basis, while the decision tree (DT) showed significant performance with an R² of 0.81. Support vector regression (SVR) performed well with an R² of 0.96, highlighting its ability to capture the non-linear nuances of the data. Given the complexity inherent in the observed relationships, characterized by non-linear variations, we opted for a combined approach. K-means, closely linked to SVR, was integrated for its ability to identify homogeneous subgroups in the data (R2 up to 0.98). This combination made it possible to circumvent the limits of strictly linear methods, thus reinforcing the robustness of our analysis. These results underline the capacity of the chosen methodology to decode the interactions between NDVI and wheat production in the complex context of Taounate. By providing clear and nuanced perspectives, this study helps inform agricultural decisions and build resilience to climate challenges in the region.
Volume: 37
Issue: 1
Page: 648-659
Publish at: 2025-01-01

A novel boundary adaptive oversampling approach for intrusion detection

10.11591/ijeecs.v37.i1.pp518-529
Ritinder Kaur , Neha Gupta
Managing unbalanced datasets is a significant challenge in intrusion detection, since uncommon assaults are often obscured by the bulk of regular network traffic. In order to mitigate the effects of class imbalance and improve intrusion detection system (IDS) performance, it is necessary to use a variety of imbalanced learning algorithms. Methods of data augmentation such as adaptive synthetic sampling (ADASYN) and synthetic minority oversampling technique (SMOTE) are useful in addressing class imbalance. This paper introduces a novel technique to data resampling where decision tree-generated decision boundaries are used to conduct ADASYN on complicated and unusual samples. When this method’s efficacy was evaluated using the standard NSL-KDD dataset, the accuracy of the unusual class u2r was increased to 42% and, for r2l it was improved to 83%, respectively. The UNSW-NB 15 dataset has been used for further validation of the method, and its statistical significance has been asserted by comparing the suggested method to other oversampling techniques.
Volume: 37
Issue: 1
Page: 518-529
Publish at: 2025-01-01

Enhanced SMS spam classification using machine learning with optimized hyperparameters

10.11591/ijeecs.v37.i1.pp356-364
Nasereddine Hafidi , Zakaria Khoudi , Mourad Nachaoui , Soufiane Lyaqini
Short message service (SMS) text messages are indispensable, but they face a significant issue with spam. Therefore, there is a need for robust models capable of classifying SMS messages as spam or non-spam. Machine learning offers a promising approach for this classification, based on existing datasets. This study explores a comparison of several techniques, including logistic regression (LR), support vector machines (SVM), gradient boosting (GB), and neural networks (NN). Hyperparameters play a crucial role in the performance of these models, and their optimization is essential for achieving high accuracy. To this end, we employ an evolutionary programming approach for hyperparameter optimization. This approach evaluates the performance of these models before and after hyperparameter optimization, aiming to identify the most effective model for SMS spam classification.
Volume: 37
Issue: 1
Page: 356-364
Publish at: 2025-01-01

Archimedes assisted LSTM model for blockchain based privacy preserving IoT with smart cities

10.11591/ijeecs.v37.i1.pp488-497
Sanjaikanth E Vadakkethil Somanathan Pillai , Rohith Vallabhaneni , Srinivas A Vaddadi , Santosh Reddy Addula , Bhuvanesh Ananthan
Presently, the emergence of internet of things (IoT) has significantly improved the processing, analysis, and management of the substantial volume of big data generated by smart cities. Among the various applications of smart cities, notable ones include location-based services, urban design and transportation management. These applications, however, come with several challenges, including privacy concerns, mining complexities, visualization issues and data security. The integration of blockchain (BC) technology into IoT (BIoT) introduces a novel approach to secure smart cities. This work presents an Archimedes assisted long short-term memory (LSTM) model intrusion detection for BC based privacy preserving (PP) IoT with smart cities. After the stage of pre-processing, the LSTM is utilized for automated feature extraction and classification. At last, the Archimedes optimizer (AO) is utilized to optimize the LSTM’s hyper-parameters. In addition, the BC technology is utilized for securing the data transmission.
Volume: 37
Issue: 1
Page: 488-497
Publish at: 2025-01-01
1 2 3
...
2097 2098
Show 378 of 2098

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration