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An improved reptile search algorithm-based machine learning for sentiment analysis

10.11591/ijece.v15i1.pp755-766
Nitesh Sureja , Nandini M. Chaudhari , Jalpa Bhatt , Tushar Desai , Vruti Parikh , Sonia Panesar , Heli Sureja , Jahnavi Kharva
The rapid growth of mobile technologies has transformed social media, making it crucial for expressing emotions and thoughts. When making significant decisions, businesses and governments can benefit from understanding public opinion. This information makes sentiment analysis vital for understanding public sentiment polarity. This study develops a hyper tuned deep learning model with swarm intelligence and many approaches for sentiment analysis. convolutional neural network (CNN), bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), CNN-LSTM, BERT-LSTM, and BERT-CNN are the six deep learning models of the sentiment analysis using deep learning with reinforced learning based on reptile search algorithm (SA-DLRLRSA) model. The reptile search algorithm, an enhanced swarm intelligence algorithm (SIA), optimizes deep learning model hyper parameters. Word2Vec word embedding is used to convert textual input sequences to representative embedding spaces. Pre-trained Word2Vec embedding is also used to address issue of unbalanced datasets. Experimental results demonstrate that the SA-DLRLRSA model works best with accuracies of 93.1%, 94.7%, 96.8%, 96.3%, 97.2%, and 98.3% utilizing CNN, LSTM, BERT, CNN-LSTM, BERT-CNN, and BERT-LSTM.
Volume: 15
Issue: 1
Page: 755-766
Publish at: 2025-02-01

Tomato plant disease prediction system with a new framework SSMAN using advanced deep learning techniques

10.11591/ijece.v15i1.pp940-948
Saravanan Madderi Sivalingam , Lakshmi Devi Badabagni
Agriculture plays a pivotal role in India's economy, and the timely detection of plant infections is essential to safeguard crops and prevent further spread of diseases. The conventional approach involves manual inspection of plant leaves to identify the specific type of disease, a task typically carried out by farmers or plant pathologists. In previous studies, you only look once (YOLO) and faster region-based convolutional neural network (R-CNN), machine learning algorithms were applied to datasets for detecting objects on tomato leaves which includes a total of images 2403 and got accuracies of 86 and 82 percent. In this paper, a deep convolutional neural network (DCNN) model proposed with a new framework separate, shift, and merge based AlexNet50 algorithm (SSMAN) is used to predict the disease at an earlier stage with higher accuracy. Among various pre-trained deep models, AlexNet emerges as the top performer, achieving the highest accuracy in disease classification. SSMAN can address anomalies in images by employing a class decomposition approach to scrutinize class boundaries. AlexNet exhibits a notable accuracy of 98.30% in successfully identifying tomato leaf diseases from images, with pre-trained new framework, superior to the original AlexNet architecture as well as traditional classification methods with other algorithms.
Volume: 15
Issue: 1
Page: 940-948
Publish at: 2025-02-01

Overcoming imbalanced rice seed germination classification: enhancing accuracy for effective seedling identification

10.11591/ijai.v14.i1.pp62-72
Muhlasah Novitasari Mara , Sidiq Syamsul Hidayat , Farika Tono Putri , Dwi Rahmawati , Sri Wahyuni , Muhamad Cahyo Ardi Prabowo , Noer Ni'mat Syamsu Kabir , Ragil Tri Indra
This study aimed to automatically classify rice seedling germination on day seven using image analysis. The categories included normal, abnormal, and dead seeds. Due to the rarity of abnormal seedlings, capturing their images resulted in imbalanced data. To address this, abnormal categories were combined into a single class. We compared logistic regression, random forest, and deep learning models (VGG19, VGG16, Alex Net) for classification. Surprisingly, logistic regression achieved the highest accuracy (93.89%) and F1-scores (0.96 normal, 0.81 abnormal) despite the imbalanced data and complex task. The effectiveness of logistic regression for rice seedling classification with imbalanced data has been demonstrated in this novel research. Historically, deep learning models dominate image recognition, but our findings suggest simpler models can excel in specific scenarios, especially with limited data availability. This highlights the importance of selecting models based on data characteristics. The urgency for this research stems from the need for efficient and accurate rice seedling evaluation. Improved classification can enhance agricultural practices and optimize resource allocation.
Volume: 14
Issue: 1
Page: 62-72
Publish at: 2025-02-01

Integration of web scraping, fine-tuning, and data enrichment in a continuous monitoring context via large language model operations

10.11591/ijece.v15i1.pp1027-1037
Anas Bodor , Meriem Hnida , Najima Daoudi
This paper presents and discusses a framework that leverages large-scale language models (LLMs) for data enrichment and continuous monitoring emphasizing its essential role in optimizing the performance of deployed models. It introduces a comprehensive large language model operations (LLMOps) methodology based on continuous monitoring and continuous improvement of the data, the primary determinant of the model, in order to optimize the prediction of a given phenomenon. To this end, first we examine the use of real-time web scraping using tools such as Kafka and Spark Streaming for data acquisition and processing. In addition, we explore the integration of LLMOps for complete lifecycle management of machine learning models. Focusing on continuous monitoring and improvement, we highlight the importance of this approach for ensuring optimal performance of deployed models based on data and machine learning (ML) model monitoring. We also illustrate this methodology through a case study based on real data from several real estate listing sites, demonstrating how MLflow can be integrated into an LLMOps pipeline to guarantee complete development traceability, proactive detection of performance degradations and effective model lifecycle management.
Volume: 15
Issue: 1
Page: 1027-1037
Publish at: 2025-02-01

Cloud computing environment based hierarchical anomaly intrusion detection system using artificial neural network

10.11591/ijece.v15i1.pp1209-1217
Mangalapalli Vamsikrishna , Garapati Swarna Latha , Gajjala Venkata Ramesh Babu , Koppisetti Giridhar , Lakshmeelavanya Alluri , Giddaluru Somasekhar , Bhimunipadu Jestadi Job Karuna Sagar , Naresh Dondapati
Nowadays, computer technology is essential to everyday life, including banking, education, entertainment, and communication. Network security is essential in the digital era, and detecting intrusion threats is the most difficult problem. As a result, the network is monitored for unusual activity using this hierarchical anomaly intrusion detection system, and when these actions are detected, an alert is generated. This hierarchical anomaly intrusion detection system, which uses artificial neural network (ANN) and is implemented on a cloud computing environment, analyzes data even in the high levels of traffic and protects computer networks and data from malicious activity. As a result, this system shows better detection, accuracy, and precision rates.
Volume: 15
Issue: 1
Page: 1209-1217
Publish at: 2025-02-01

Comparison of faster region-based convolutional network for algorithms for grape leaves classification

10.11591/ijai.v14.i1.pp222-230
Moechammad Sarosa , Puteri Nurul Ma'rifah , Mila Kusumawardani , Dimas Firmanda Al Riza
The shapes of leaves distinguish the Indonesian grape variants. The grape leaves might look the same at first glance, but there are differences in leaf shapes and characteristics when observed closely. This research uses a deep learning method combined with the faster region-based convolutional neural network (R-CNN) algorithm with the Inception network architecture, ResNet V2, ResNet-152, ResNet-101, and ResNet-50, and uses COCO weights trained to classify five grape varieties through leaf images. The study collected 500 images to be used as an independent dataset. The results show that network improvements can effectively improve operating efficiency. There are also limitations to training scores because the F1 score value tends to stabilize or decrease at a certain point. In the Inception ResNet V2 architecture, with the highest average F1 score of 92%, the average computing time for training and testing is longer than other network architectures. This suggests that the algorithm can classify types of grapes based on their leaves.
Volume: 14
Issue: 1
Page: 222-230
Publish at: 2025-02-01

Balancing and metaheuristic techniques for improving machine learning models in brain stroke prediction

10.11591/ijai.v14.i1.pp473-481
Abd Allah Aouragh , Mohamed Bahaj , Fouad Toufik
A brain stroke, medically referred to as a stroke, represents a critical condition triggered by the disruption of blood flow to a region of the brain. Early detection of stroke is crucial to prevent fatal complications. In this study, we worked with an unbalanced dataset of 4981 entries on stroke, which we balanced using the K-means synthetic minority over-sampling technique (KMeansSMOTE) algorithm. We then employed five machine learning algorithms: decision tree, random forest, support vector machine, K-nearest neighbors, and gradient boosting. We compared the hyperparameter optimization of these algorithms using four metaheuristic techniques: gray wolf optimization, particle swarm optimization, genetic algorithm, and artificial bee colony. The models' effectiveness was evaluated using multiple metrics, such as accuracy, recall, precision, F1-score, and area under the receiver operating characteristic curve. Our findings indicate that the random forest optimized by the genetic algorithm achieved the best performance, with an accuracy of 97.39% and an F1-score of 97.35%. This study highlights the effectiveness of balancing and metaheuristics techniques in optimizing machine learning models for stroke forecasting.
Volume: 14
Issue: 1
Page: 473-481
Publish at: 2025-02-01

Large language models-based metric for generative question answering systems

10.11591/ijai.v14.i1.pp151-158
Hazem Abdel Azim , Mohamed Tharwat Waheed , Ammar Mohammed
In the evolving landscape of text generation, which has advanced rapidly in recent years, techniques for evaluating the performance and quality of the generated text lag behind relatively. Traditionally, lexical-based metrics such as bilingual evaluation understudy (BLEU), recall-oriented understudy for gisting evaluation (ROUGE), metric for evaluation of translation with explicit ordering (METEOR), consensus-based image description evaluation (CIDER), and F1 have been utilized, primarily relying on n-gram similarity for evaluation. In recent years, neural and machine-learning-based metrics, like bidirectional encoder representations from transformers (BERT) score, key phrase question answering (KPQA), and BERT supervised training of learned evaluation metric for reading comprehension (LERC) have shown superior performance over traditional metrics but suffered from a lack of generalization towards different domains and requires massive human-labeled training data. The main contribution of the current research is to investigate the use of train-free large language models (LLMs) as scoring metrics, evaluators, and judges within a questionanswering context, encompassing both closed and open-QA scenarios. To validate this idea, we employ a simple zero-shot prompting of Mixtral 8x7 B, a popular and widely used open-source LLM, to score a variety of datasets and domains. The experimental results on ten different benchmark datasets are compared against human judgments, revealing that, on average, simple LLMbased metrics outperformed sophisticated state-of-the-art statistical and neural machine-learning-based metrics by 2-8 points on answer-pairs scoring tasks and up to 15 points on contrastive preferential tasks.
Volume: 14
Issue: 1
Page: 151-158
Publish at: 2025-02-01

Deep learning for skin melanoma classification using dermoscopic images in different color spaces

10.11591/ijece.v15i1.pp319-327
Sankarakutti Palanichamy Manikandan , Sandeep Reddy Narani , Sakthivel Karthikeyan , Nagarajan Mohankumar
Skin cancer begins in the skin cells. The damage to the skin cells can cause genetic mutations that lead to uncontrolled growth and the formation of tumors. It is estimated that millions of people are diagnosed with skin cancer of different kinds each year. The earlier a skin cancer is diagnosed, the better the patient's prognosis and the lower their chance of complications. In this work, an efficient deep learning classification (EDLCS) to classify dermoscopic images is developed. The importance of color in the diagnosis of skin melanoma has caused color analysis to attract considerable attention from researchers of image-based skin melanoma analysis. Three different color spaces such as red-green-blue (RGB), hue-saturation-lightness (HIS) and LAB are investigated in this study. The obtained dermoscopic images are in RGB color space. The RGB dermoscopic images are first converted into HSV and LAB spaces to investigate the HSV and LAB color spaces for melanoma classification. Then, the color space converted image is fed to the proposed EDLCS to evaluate their performances. Results show that the proposed EDLCS provides 99.58% accuracy while using the LAB color model to classify preprocessed images while other models provide 99.17%.
Volume: 15
Issue: 1
Page: 319-327
Publish at: 2025-02-01

Analytic hierarchy process geographic information system based model for sustainable construction and demolition waste landfill site selection

10.11591/ijece.v15i1.pp803-816
Mohamed Ayet Allah Bilel Soussi , Nermine El Madsia , Chamseddine Zaki , Alaaeddine Ramadan , Louai Saker , Moustafa Ibrahim
Properly managing waste generated by buildings and public works is a significant challenge in Tunisia, particularly in the city of Bizerte. The inadequate disposal of such waste can cause substantial harm to human life, property, and the environment. This paper proposes an multi-criteria decision making (MCDM) that utilizes the analytic hierarchy process (AHP) decision support tool to identify suitable landfill sites for construction and demolition waste (CDW) in Bizerte. The AHP method is widely used in MCDM applications. The approach involves classifying different scenarios based on various exclusion and appreciation criteria to determine the optimal locations for future landfills. Furthermore, the paper develops a conceptual approach for identifying better sites for the disposal of CDW, resulting in a comprehensive database capable of storing, accessing, and extracting information at both conceptual and operational levels. The proposed model considers spatial, technical, and environmental criteria in the selection of a suitable landfill site. The proposed methodology offers an effective and practical solution for properly managing CDW waste in Bizerte, Tunisia, and can be applied to other regions facing similar challenges.
Volume: 15
Issue: 1
Page: 803-816
Publish at: 2025-02-01

A revolutionary convolutional neural network architecture for more accurate lung cancer classification

10.11591/ijai.v14.i1.pp516-526
Muliadi Muliadi , Agus Perdana Windarto , Solikhun Solikhun , Putrama Alkhairi
This research aimed to investigate a breakthrough in convolutional neural network (CNN) architecture with the potential to revolutionize lung cancer classification. The proposed method is a comparative optimization model of ResNet architecture, with accuracy rate of 99.68% in identifying and categorizing lung cancer types. The results showed that the use of innovative methods in CNN architecture, such as multi-dimensional convolutional layers and the integration of specific lung cancer features, effectively provided highly accurate and reliable outcomes. These showed a positive impact on the development of medical diagnostic technology, offering promising potential to enhance prognosis and response to treatment for lung cancer patients. With high accuracy rate, this breakthrough presents opportunities for further advancements in lung cancer management through artificial intelligence-based methods.
Volume: 14
Issue: 1
Page: 516-526
Publish at: 2025-02-01

From concept to application: building and testing a low-cost light detection and ranging system for small mobile robots using time-of-flight sensors

10.11591/ijece.v15i1.pp292-302
Andrés García , Mauricio Díaz , Fredy Martínez
Advancements in light detection and ranging (LiDAR) technology have significantly improved robotics and automated navigation. However, the high cost of traditional LiDAR sensors restricts their use in small-scale robotic projects. This paper details the development of a low-cost LiDAR prototype for small mobile robots, using time-of-flight (ToF) sensors as a cost-effective alternative. Integrated with an ESP32 microcontroller for real-time data processing and Wi-Fi connectivity, the prototype facilitates accurate distance measurement and environmental mapping, crucial for autonomous navigation. Our approach included hardware design and assembly, followed by programming the ToF sensors and ESP32 for data collection and actuation. Experiments validated the accuracy of the ToF sensors under static, dynamic, and varied lighting conditions. Results show that our low-cost system achieves accuracy and reliability comparable to more expensive options, with an average mapping error within acceptable limits for practical use. This work offers a blueprint for affordable LiDAR systems, expanding access to technology for research and education, and demonstrating the viability of ToF sensors in economical robotic navigation and mapping solutions.
Volume: 15
Issue: 1
Page: 292-302
Publish at: 2025-02-01

Berkeley wavelet transform and improved YOLOv7-based classification technique for brain tumor severity prediction

10.11591/ijece.v15i1.pp958-969
Nilesh Bhaskarrao Bahadure , Sidheswar Routray , Jagdish Chandra Patni , Nagrajan Raju , Prasenjeet Damodar Patil
Abnormality in brain tissues is a life-threatening illness in humans Un-bias to gender and age if it is unrecognized and untreated within time, will lead to severe complications and extreme conditions. The brain tumor is mainly influenced by a variety of unpredicted and unavoidable reasons. Its evaluation, spread pattern, and identification involves complex assignment. Its early grading and the proper classification ensure effective treatment. The proposed work attempts to extract and classify the tumor region using an automatic classification system for magnetic resonance imaging (MRI) brain tumors. A deep learning convolutional neural network-based architecture YOLO is employed to classify and detect the tumor from brain MR images. The proposed method resulted in superior segmentation, and classification performance in terms of subjective visualization and objective metrics as compared to state of art approaches. The proposed YOLO-based method collectively achieved 98.89% classification accuracy on the BRAINIX and Kaggle datasets.
Volume: 15
Issue: 1
Page: 958-969
Publish at: 2025-02-01

Quadratic multivariate linear regressive distributed proximity feature engineering for cybercrime detection in digital fund transactions with big data

10.11591/ijece.v15i1.pp689-699
Arul Jeyanthi Paulraj , Balaji Thalaimalai
Digital fund transactions involve the electronic transfer of funds between parties through digital channels such as online banking platforms, mobile applications, and electronic payment systems. However, the rapid advancement of digital transactions has also directed cybercriminals to exploit vulnerabilities, engaging in money laundering and other illegal activities, resulting in substantial financial losses. The improve accuracy of cybercriminal detection by lesser time consumption, a novel technique called quadratic multivariate linear regressive distributed proximity feature engineering (QMLRDPFE) is developed. The proposed QMLRDPFE technique comprises two primary steps namely data preprocessing and feature engineering. Analyzed results prove that the QMLRDPFE technique outperforms existing methods in attaining superior accuracy and precision. Furthermore, QMLRDPFE method shows effective in reducing time utilization and space complexity for fraudulent transaction detection compared to existing approaches. Results to provide effective in reducing time utilization and space complexity for fraudulent transaction detection than the conventional methods.
Volume: 15
Issue: 1
Page: 689-699
Publish at: 2025-02-01

Enhancing PETRONAS share price forecasts: a hybrid Holt integrated moving average

10.11591/ijece.v15i1.pp728-740
Nurin Qistina Mohamad Fozi , Nurhasniza Idham Abu Hasan , Azlan Abdul Aziz , Siti Meriam Zahari , Mogana Darshini Ganggayah
Understanding the variations in PETRONAS share price over time is important for improving the forecast accuracy of PETRONAS share prices to provide stakeholders with reliable analyses for future market predictions. Therefore, the main objective of this study is to improve the accuracy of PETRONAS share price by utilizing a hybrid Holt method with the moving average (MA) from the Box-Jenkins model. Holt's method will address linear trends for non-stationary data, while MA will analyze residual aspects of the data. This combination transforms non-stationary data into stationary by removing noise and averaging out fluctuations. The secondary data used in this study consists of daily observation from bursa Malaysia, the official national stock exchange of Malaysia, covering the period from January 3, 2000, to October 2, 2023. The study encompasses both low and high share price scenarios. The models’ performance was compared using various error metrics across different training and testing splits. The findings highlight that the proposed hybrid [Holt–MA] model called Holt integrated moving average (HIMA) improves the accuracy of forecasting model with the smallest errors for both daily low and high share price. The HIMA model demonstrates significant potential, particularly in reducing residuals and improving prediction accuracy.
Volume: 15
Issue: 1
Page: 728-740
Publish at: 2025-02-01
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