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IR4.0 and internet of things: future directions towards enhanced connectivity, automation, and sustainable innovation

10.12928/telkomnika.v22i6.25487
Yousef A. Baker; Universitas Sultan Zainal Abidin El-Ebiary , Salameh A.; Al-Balqa Applied University (BAU) Mjlae , Hadeel; Applied Science Private University (ASPU) Ahmad , Suzie Y.; Jadara University Rababa'h , Mahmoud A.; Jadara University Rababah , Omar G.; Al-Balqa Applied University Arabiat
This study provides a systematic review of the literature on the fourth industrial revolution (IR4.0) and the internet of things (IoT) in genetics, informatics, and biotechnology, as well as their many rapidly evolving applications, which often overlap with various aspects of life. The study reviews the latest research, books, scientific theses, and official websites in this field, placing them in a coherent context for researchers. It covers innovations that have driven the continuous development of IR4.0 and its challenges. The study highlights numerous fields and modern technologies expected to bring about tangible and radical changes in health, agriculture, and industry. It addresses key aspects of IR4.0 and the significant advancements brought by smart cities, and most importantly, artificial intelligence (AI), including robots, autonomous cars, 3D printing, big data, IoT, nanotechnology, biotechnology, energy storage, and quantum computing. By applying cutting-edge technology across various disciplines to boost productivity and foster development, technology plays a crucial role in linking the physical, digital, and biological realms. This revolution is built on multiple axes, including the convergence of biotechnology, digital media, and physical systems.
Volume: 22
Issue: 6
Page: 1469-1477
Publish at: 2024-12-01

Optimal sizing and economic assessment of diesel and solar mills for cereal milling in rural areas of Senegal

10.11591/ijpeds.v15.i4.pp2526-2534
Moustapha Diop , Mouhamadou Falilou Ndiaye , Ibrahima Gueye , Abdoulaye Kebe , Omar Dia
This paper aims at using life cycle costing (LCC) to assess the economic viability of solar and diesel mills for cereal milling, adopting several scenarios of village sizes in Senegal and time perspectives. This work is intended to provide decision-makers and stakeholders with valuable information for selecting optimal milling systems, taking into account both economic and village size considerations. The methodology adopted is based on system specifications and daily cereal consumption profiles. To determine the economic performance of systems, three types of villages are evaluated and analyzed in detail from short, medium, and long-term perspectives. The results indicate that solar mills are the most economical systems for milling cereals in remote villages in the long term, with a potential economic gain of 10%, 31%, and 41% for small, medium, and big villages respectively, compared to diesel mills. However, solar mills are less promising in the short term.
Volume: 15
Issue: 4
Page: 2526-2534
Publish at: 2024-12-01

Machine learning prediction of quality of life: Insight from property crime and tropical climate analysis

10.11591/ijai.v13.i4.pp4509-4515
Anis Zulaikha Mohd Zukri , Siti Rasidah Md Sakip , Suraya Masrom
The study addresses the prediction of quality of life, leveraging machine learning models with a focus on health, socioeconomics, subjective well-being, and environmental indicators. Thus, this study aims to evaluate the efficacy of machine learning in quality-of-life prediction based on property crime and temperature. Five machine learning algorithms were used to be empirically compared namely generalized linear model (GLM), random forest (RF), decision tree (DT), gradient boosted tree (GBT) and support vector machine (SVM) are compared empirically. The performance of each machine learning algorithm in predicting the quality of life has been observed based on the attributes of property crime and tropical climate (temperature). Despite initial low correlation with quality of life, temperature significantly contributes to specific algorithms, enhancing predictive accuracy. This shows the complexity of machine learning impacts. SVM emerges as the best-performing algorithm, followed by RF and DT. The findings highlight the importance of seemingly unrelated factors in prediction outcomes. This paper presents a fundamental research framework useful for helping educators and researchers to explore in depth quality of life prediction with using property crime and temperature as a factor. 
Volume: 13
Issue: 4
Page: 4509-4515
Publish at: 2024-12-01

Non-contact power system fault diagnosis: a machine learning approach with electromagnetic current sensing

10.11591/ijeecs.v36.i3.pp1356-1364
Amit L. Nehete , Deepak S. Bankar , Ritika Asati , Chetan Khadse
Modern power system protection schemes incorporate artificial intelligence (AI) techniques. However, in a conventional way, most of these schemes rely on the data of current and voltage collected from current transformer (CT) and potential transformer (PT) respectively. CTs suffer from the drawback of core saturation and impact the accuracy and effectiveness of intelligent methods. Also, it has the constraints of size, safety, and economy. The research here explores the effectiveness of magnetic sensors in advanced power system protection schemes as an alternative to traditional current sensing. In the presented work, a novel dataset is prepared by transforming transmission line currents into magnetic field components. Several supervised as well as unsupervised machine learning algorithms have been applied to this data instead of traditional currents and voltage for fault prediction. The paper discusses the comparative evaluation of these algorithms based on various performance metrices which reveals that Gaussian Naïve Bayes (GNB), K-nearest neighbor (KNN), random forest (RF), and extreme gradient boost (XGB) algorithms excel in fault detection, while multilayer perceptron (MLP) and KNN performs better fault classification. The findings promise the potential for developing compact, safe, and cost-effective protection schemes utilizing magnetic field sensors.
Volume: 36
Issue: 3
Page: 1356-1364
Publish at: 2024-12-01

Proactive cervical cancer risk assessment using data-driven analytics

10.11591/ijai.v13.i4.pp4301-4311
Sreelatha Sreelatha , Vrinda Shivashetty
This study introduces a sophisticated predictive model integrating clinical and lifestyle data addressing the critical public health challenge of cervical cancer, particularly in regions lacking routine screenings. Leveraging data driven analytics, the proposed model undergoes comprehensive preprocessing, including exploratory data analysis, missing value imputation, and feature extraction. Feature selection is carried out using the XGBoost classifier to ensure model efficacy. Data normalization and class balance via oversampling techniques are applied, with model validation conducted through stratified cross-validation. The optimized feature vector is then employed to train a LightGBM model. Utilizing a retrospective dataset of 858 patients from the Hospital Universitario de Caracas, Venezuela, comprising demographic, lifestyle, and medical history data, the LightGBM model achieves an impressive accuracy of 98%, outperforming similar existing approaches. The study outcome demonstrates the effectiveness of the proposed data modelling framework and feature selection, along with the choice of LightGBM as a suitable classifier. The proposed predictive framework can efficiently aid healthcare professionals in prioritizing high-risk patients for further evaluation and intervention.
Volume: 13
Issue: 4
Page: 4301-4311
Publish at: 2024-12-01

Detection and identification of un-uniformed shape text from blurred video frames

10.11591/ijai.v13.i4.pp4795-4805
Ravikumar Hodikehosahally Channegowda , Raghavendra Srinivasaiah , Santosh Kumar Jankatti , Meenakshi B , Niranjana Shravanabelagola Jinachandra , Raveendra Kumar Tavarekere Hombegowda
The identification and recognition of text from video frames have received a lot of attention recently, that makes many computer vision-based applications conceivable. In this study, we modify the picture mask and the original identification of the mask region convolution neural network and permit detection in three levels, including holistic, sequence, and at the level of pixels. To identify the texts and determine the text forms, semantics at the pixel and holistic levels can be used. With masking and detection, existences of the character and the word are separated and recognised. In addition, text detection using the results of 2-D feature space instance segmentation is done. Moreover, we explore text recognition using an attention-based optical character recognition (OCR) method with mask region convolution neural networks (R-CNN) to address and detect the problem of smaller and blurrier texts at the sequential level. Using attribute maps of the word occurrences in sequence to seq, the OCR method calculates the character sequence. At last, a fine-grained learning strategy is proposed to constructs models at word level using the annotated datasets, resulting in the training of a more precise and reliable model. The well-known benchmark datasets ICDAR 2013 and ICDAR 2015 are used to test our suggested methodology.
Volume: 13
Issue: 4
Page: 4795-4805
Publish at: 2024-12-01

Accuracy based-stacked ensemble learning model for the prediction of coronary heart disease

10.11591/ijai.v13.i4.pp4516-4525
Santosini Bhutia , Bichitrananda Patra , Mitrabinda Ray
Coronary heart disease (CHD) is the primary cause of silent and noncommunicable deaths. Early detection is essential for slowing the progression of death and saving lives. Medical researchers use machine learning techniques to predict CHD. This article proposes an accuracy based-stacked ensemble learning (AB-SEL) model to predict CHD while minimizing computational time (CT). The dataset undergoes the logistic regression recursive feature elimination (LR-RFE) method to identify the important features. The three strong classifiers, logistic regression (LR), random forest (RF), and AdaBoost, are chosen to build ensemble machine-learning models, including techniques like bagging, majority voting, and stacking, for the Cleveland dataset accessible from Kaggle. Data scaling was done using the normal scalar method, and hyperparameter optimization was done using random search and grid search. Effectiveness is measured by accuracy, precision, recall, F1 score, and CT is validated through 5-fold cross-validation. The suggested approach achieved a 90.16% accuracy rate, required only 0.2 seconds of CT, and yielded an area under the curve (AUC) of 0.892.
Volume: 13
Issue: 4
Page: 4516-4525
Publish at: 2024-12-01

Neuroeducation and impact on higher education: a systematic review

10.11591/ijere.v13i6.29170
Sebastián Huangal-Scheineder , Jaime Cieza-Sánchez , Manuel Diaz-Paredes , Miguel Arriaga-Delgado , Aurora Marchena-Tafur
The educational system is always seeking to optimize the teaching-learning process through new methodologies. In this sense, there is an effort to improve the student’s academic performance through the acquisition of the desired knowledge that will also last in time. Given this reality, neuroeducation is born as a contribution to the development of the educational system within a scientific basis that understands the way people learn. In this context, the objectives were to determine the impact of neuroeducation in higher education and to describe the current state of strategies based on neuroeducation in the higher educational context. Therefore, a systematic review of the literature was carried out applying the PRISMA method, establishing a periodic range of 5 years (2018-2023). The articles selected in this review were 47 documents. The results showed that this discipline has many benefits for students to learn properly, although teachers have little knowledge about it. Finally, it is concluded that neuroeducation brings with it strategies to improve teaching based on stimuli and motivation, thus achieving a good academic performance by the student.
Volume: 13
Issue: 6
Page: 3641-3652
Publish at: 2024-12-01

Tomato leaf disease recognition system using Faster R-CNN

10.12928/telkomnika.v22i6.25519
Karel Octavianus; Atma Jaya Catholic University of Indonesia Bachri , Bryan; Atma Jaya Catholic University of Indonesia Santoso , Duma Kristina; Atma Jaya Catholic University of Indonesia Yanti Hutapea , Catherine Olivia; Atma Jaya Catholic University of Indonesia Sereati , Lanny W.; Atma Jaya Catholic University of Indonesia Pandjaitan
The objective of this paper is to detect tomato leaf disease using Faster region-based convolutional neural network (R-CNN). The tomato leaf disease recognition system utilizes a dataset consisting of healthy tomato leaves and eight leaf diseases, including early blight, late blight, leaf mold, mosaic virus, septoria, spider mites, yellow leaf curl virus, and leaf miner. The dataset is obtained from various sources, such as Kaggle, Google Images, Bing Images, and Roboflow Universe. Pre-processing techniques, including collage, tile, static crop, and resize, are applied to prepare the dataset for training. Data augmentation methods, such as flipping, 90° rotation, exposure adjustment, and hue modification, are applied to enhance the model’s robustness and generalize its performance. Specifically, we implemented Faster R-CNN as part of Detectron2 using its base models and configurations. The results demonstrate that the X101-FPN base model for Faster R-CNN with the default configurations of Detectron2 is efficient and general enough to be applied to defect detection. This approach results in an average precision (AP) detection score of 87.01% for validation results.
Volume: 22
Issue: 6
Page: 1414-1421
Publish at: 2024-12-01

Colour sorting ROS-based robot evaluation under different lights and camera angles

10.11591/ijeecs.v36.i3.pp1807-1815
Mohammad Farid Saaid , Norashikin M. Thamrin , Mohamad Farid Misnan , Roslina Mohamad , Nurul A’qilah Romli
Automated colour sorting, aided by mobile robots, is widely prevalent in the current manufacturing industry. Obstacles, such as fluctuating light conditions and camera angles, frequently hinder this procedure. Creating a colour sorting robot is a complex and time-consuming task, especially due to the vulnerability of the RGB colour space to detection errors in extreme brightness or darkness. In response to these concerns, we introduce a mobile robot that operates on the robot operating system (ROS) platform and incorporates OpenCV. This robot employs the hue, saturation, and value (HSV) colour space model for its image processing capabilities in recognising the colours and Welzl’s algorithm for the ball’s diameter estimation. The robot’s performance was assessed across various luminous fluxes and camera tilt angles. It demonstrated exceptional performance at 64 lm and a tilt angle of 40 degrees, achieving an average accuracy of 87.5% for detecting the colour of the ball, and 81.25% for determining its location based on colour. For the ball’s diameter estimation, it was found that the best estimation was received at 64 lm and 30 degrees, with both 96.32%.
Volume: 36
Issue: 3
Page: 1807-1815
Publish at: 2024-12-01

Optimized multi-layer self-attention network for feature-level data fusion in emotion recognition

10.11591/ijai.v13.i4.pp4435-4444
Basamma Umesh Patil , Ashoka Davanageri Virupakshappa , Ajay Prakash Basappa Vijaya
Understanding human emotions across diverse data sources presents challenges in various applications including healthcare, human-machine interaction, security, marketing, and gaming. Prior research has explored fusion techniques to address multimodal data heterogeneity, yet often overlooks the importance of discriminative unimodal information and potential complementarity among fusion strategies. Recognizing emotions from video and audio data poses challenges such as non-verbal cues interpretation, varying expression, ambiguity in context, and the need for nuanced feature extraction to capture subtle emotional nuances accurately. To tackle these issues, it is imperative to employ efficient emotion representation and multimodal fusion techniques, as these tasks have significant importance within the realm of multifaceted recognizing study. This study introduced a novel approach, optimized multi-layer self-attention network for emotion recognition (OMSN-ER), focusing on feature-level data fusion. OMSN-ER precisely assesses emotional states by merging facial and voice data, utilizing a multi-layer progressive dense residual fusion network and a self-attention mountain gazelle convolution neural network. Implemented in Python with the RAVDESS dataset, the methodology achieves exceptional accuracy (0.9908), surpassing benchmarks and demonstrating efficacy in multimodal emotion recognition. This research represents promising advancements in the intricate field of emotion recognition.
Volume: 13
Issue: 4
Page: 4435-4444
Publish at: 2024-12-01

The game model of investing in the academic cloud

10.12928/telkomnika.v22i6.26121
Bakhytzhan; National University of Life and Environmental Sciences of Ukraine Akhmetov , Valery; National University of Life and Environmental Sciences of Ukraine Lakhno , Nurzhamal; Kazakh National Pedagogical University named after Abai Oshanova , Volodimir; National University of Life and Environmental Sciences of Ukraine Malyukov , Zhuldyz; Satbayev University Alimseitova , Aliza; Almaty Technological University Turgnbayeva , Inna; National University of Life and Environmental Sciences of Ukraine Malyukova , Miroslav; National University of Life and Environmental Sciences of Ukraine Lakhno
The article analyzes approaches to the use of cloud technologies in the process of teaching students at large universities. The model of the academic cloud of a modern university is considered. Examples of software and functional platforms that meet the needs of students in electronic learning resources are given. The deployment models of the cloud-oriented educational environment that includes private cloud infrastructure as a service (IaaS) and platform as a university service are analyzed. The cost of deploying an academic cloud based on the educational institution’s infrastructure and renting infrastructure from a vendor is compared. A multifactorial model for evaluating investment options in the university cloud in the context of fuzzy information is proposed. In contrast to the known approaches to solving such a problem, our model assumes that the dynamics of the financial states of the players are set through a system of discrete equations. These equations describe the dynamics of multidimensional variables. The latter made it possible to consider the general problem of investing in the academic cloud within the framework of a game scheme for tasks in a fuzzy formulation, with the financial resources available to the educational institution. Preference sets and optimal financial allocation strategies for building an academic cloud are found.
Volume: 22
Issue: 6
Page: 1369-1381
Publish at: 2024-12-01

Real time Indian sign language recognition using transfer learning with VGG16

10.12928/telkomnika.v22i6.26498
Sumit; Symbiosis International (Deemed University) Kumar , Ruchi; Dr. Vishwanath Karad MIT World Peace University Rani , Sanjeev Kumar; GL Bajaj Institute of Technology and Management Pippal , Ulka; Dr. Vishwanath Karad MIT World Peace University Chaudhari
Normal people’s interaction and communication are easier than those with disabilities such as hearing and speech, which are very complicated; hence, the use of sign language plays a crucial role in bridging this gap in communication. While previous attempts have been made to solve this problem using deep learning techniques, including convolutional neural networks (CNNs), support vector machine (SVM), and K-nearest neighbours (KNN), these have low accuracy or may not be employed in real time. This work addresses both issues: improving upon prior limitations and extending the challenge of classifying characters in Indian sign language (ISL). Our system, which can recognize 23 hand gestures of ISL through a purely camera-based approach, eliminates expensive hardware like hand gloves, thus making it economical. The system yields an accuracy of 97.5% on the training dataset, utilizing a pre-trained VGG16 CNN optimized by the Adam optimizer and cross-entropy loss function. These results clearly show how effective transfer learning is in classifying ISL and its possible real-world applications.
Volume: 22
Issue: 6
Page: 1459-1468
Publish at: 2024-12-01

A robust method for detecting fake news using both machine and deep learning algorithms

10.11591/ijeecs.v36.i3.pp1816-1826
Enas Ahmad Alikhashashneh , Khalid M.O. Nahar , Mohammed Abual-Rub , Hedaya M. Alkhaldy
Spreading fake news and false information on social media is very common and can be done effortlessly due to the huge number of users of each of the various social media platforms. Another reason for having such a speedy spread of fake news (which makes about 40% of the information published on social media platforms) is the inability of these platform to verify the authenticity of the news before allowing it to be published. This research will use information technology to detect fake news/ false information and change this kind of technology from being the cause of the problem to a tool to solve it. This research provides a method that uses both machine learning (ML) and deep learning (DL) algorithms to detect fake information versus real information and compare the performance of the algorithms. The results of this research indicate that the algorithms that use term frequency inverse document frequency (TF-IDF) have achieved better results than the algorithms that use Word2Vec. Long short-term memory (LSTM) algorithm, however, has achieved the best performance; of 99% accuracy -when using TF-IDF, and 94% -when using Word2Vec.
Volume: 36
Issue: 3
Page: 1816-1826
Publish at: 2024-12-01

Enhancing road image clarity with residual neural network dehazing model

10.11591/ijai.v13.i4.pp4147-4157
Aerun Martin , Mohd Nazeri Kamaruddin , Zamani Md Sani , Hadhrami Abdul Ghani
Lane markers, or road markers, are the painted lines on a roadway that separate different lanes of traffic. Lane markers guide drivers and ensure orderly vehicle flow. They are essential for advanced driver assistance systems (ADAS), providing reference points for vehicle positioning on the road. These markers enable ADAS to give warnings, assistance, and automation features that enhance driver safety and convenience. However, unpredictable illumination, such as a foggy environment, can suppress marker visibility, impacting ADAS's performance. Deep learning-based methods are well-known for their superiority in handling various haze patterns. This paper presents a residual network (ResNet)-based deep learning model to improve road image clarity impacted by fog. The residual neural network dehaze model (RNN-D) utilises a joint loss function to produce haze-free images with improved lighting conditions and enhanced details. The model was trained, validated, and fine-tuned using hazy and corresponding non-hazy datasets to ensure that the model is quantitatively superior in the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM). RNN-D achieved an average PSNR of 27.98 and SSIM of 0.8 on multiple open sourced datasets. The proposed algorithm's superior performance and visually appealing results make it a powerful tool for real-world image dehazing applications.
Volume: 13
Issue: 4
Page: 4147-4157
Publish at: 2024-12-01
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