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31,456 Article Results

Quality and shelf-life prediction of cauliflower using machine learning under vacuum and modified atmosphere packaging

10.11591/ijai.v14.i2.pp907-916
Md. Apu Hosen , Dr. Syed Md. Galib
Ensuring the freshness and quality of cauliflower during storage and transportation is essential due to its high perishability. This study harnesses the power of machine learning to predict the quality and shelf-life of cauliflower under cost-effective vacuum and modified atmosphere packaging (MAP) techniques. By investigating key parameters such as total soluble solids (TSS), pH, weight loss, and color change, a significant impact on post-packaging quality was identified. To address the challenge of accurate color change measurement, an innovative method utilizing a bilateral filter for noise reduction and particle swarm optimization (PSO) with Markov random field (MRF) segmentation was developed. TSS, weight loss, and color change were identified as key parameters, and leveraging these parameters, artificial neural networks (ANN) were employed to create highly precise predictive models, achieving R-squared values of 0.952 for TSS, 0.992 for weight loss, and 0.981 for color change. This approach not only enhances the efficiency and sustainability of food production and distribution but also minimizes food waste and maximizes profitability for cauliflower in global markets through the use of cost-effective packaging solutions.
Volume: 14
Issue: 2
Page: 907-916
Publish at: 2025-04-01

Correlation between input and output parameters of microbial fuel cell

10.11591/ijeecs.v37.i3.pp1452-1463
Ganesan V. Murugesu , Saiful Nizam Khalid , Hussain Shareef , Saad Saleem Khan
This paper presents the correlation between open circuit voltage (OCV) and pH, temperature, and total dissolved solids (TDS) of an air cathode single chamber microbial fuel cell (MFC) using artificial neural network (ANN) and support vector machine (SVM) algorithms. Previous works used terminal voltages as output parameters to determine the correlation between MFCs' input and output parameters. However, OCV is the most important measurement that can determine the validity of the MFC. Thus, various tests were conducted to analyze the correlation between OCV and input parameters using ANN and SVM algorithms. Both techniques show a strong correlation between OCV and input parameters with the highest R2 values. The highest OCV value obtained from the experiment is 1.179 V at pH 5.26, temperature 299K, and TDS 3,124 ppm. Furthermore, an ANN model was developed to predict the OCV value based on pH, temperature, and TDS value.
Volume: 37
Issue: 3
Page: 1452-1463
Publish at: 2025-03-01

Comparative analysis of incremental conductance MPPT for enhanced algorithm performance

10.11591/ijeecs.v37.i3.pp1415-1427
Asnil Asnil , Refdinal Nazir , Krismadinata Krismadinata , Muhammad Nasir
The incremental conductance (INC) approach is limited in terms of its response speed, accuracy during steady state, and ability to handle oscillations. As a result, this algorithm is ineffective in situations with variations in solar radiation and temperature, particularly abrupt fluctuations. An enhanced variable step size INC approach is suggested to improve system efficiency and performance. The proposed algorithm is subjected to rigorous testing and analysis with other INC methods for better results. The research findings indicate that the proposed algorithm's tracking efficiency can be enhanced to 99.83% under varying radiation conditions and constant temperature. Additionally, the method utilizing the second model achieves a 99.83% tracking efficiency, while the method employing the first model achieves a tracking efficiency of 89.99%. In comparison, the conventional method achieves a tracking efficiency of 96.36%. Regarding radiation and temperature circumstances, the tracking efficiency value varies for different methods. Specifically, the tracking efficiency is 95.21% for the approach using the proposed algorithm’s, 92.94% for the method using the second model, 83,39% for the method using the first model, and 91.19% for the conventional way. Therefore, it can be inferred that the suggested Maximum power point tracking (MPPT) algorithm performs better than other algorithms under both test situations.
Volume: 37
Issue: 3
Page: 1415-1427
Publish at: 2025-03-01

Smart home advisory system based on IoT-enabled sensor network

10.11591/ijeecs.v37.i3.pp1487-1496
Mohamed Feroz Mohd Ashrap Khan , Gregory Soon How Thien , Boon Kar Yap , Kah-Yoong Chan
A consistent drive to make daily living more streamlined and less complex was critical throughout the industrial revolution. Although many applications are available, the general adoption of these applications is hindered by several factors (security concerns, expensive costs, and perceived usefulness). Hence, a cost-efficient smart home system can provide significant advantages without placing unnecessary financial or operational costs on consumers. This study proposed an inexpensive internet of things (IoT) enabled smart home advisory system for promoting healthier living conditions. The system contained numerous sensors for detecting surrounding brightness, temperature, humidity, and dust levels. Consequently, the system demonstrated effective real-time updates regarding the simulated temperature (22–32 °C), humidity (11–53 %RH), light (3–100 lx), and dust (0–278 mg/m3) environments. Blynk software was also embedded as an efficient user interface for the application. Overall, the lowcost IoT-enabled smart home advisory system offered concrete benefits by allowing users to improve their living surroundings while decreasing energy usage.
Volume: 37
Issue: 3
Page: 1487-1496
Publish at: 2025-03-01

Machine learning based optimized sea vessel location detection to identify illegal fishing

10.11591/ijeecs.v37.i3.pp1626-1636
Ajay Kumar , Kakoli Banerjee , Pradeep Kumar , Harsha K. G. , Vinooth P. , Pankaj Kumar , Saumya Saumya , Nishant Nishant , Satyam Verma , Vidushi Bhardwaj
Illegal fishing is a pervasive and destructive global issue that poses a significant threat to maritime ecosystems and the resilience of fisheries. Illegal, unregulated, and unreported (IUU) fishing leads to the extinction of the fishing population. Many researchers have presented various approaches to detect illegal fishing, for example, using sensors, image recognition, and convolutional neural networks (CNNs) but each one has some limitations. Our research aims to compare different vessel gear types to select the best vessel container that can be easily monitored and less prone to illegal activities. To achieve this, our research proposed an optimization method that involves hyperparameter selection using a genetic algorithm instead of a grid search. Using the crossover method of the genetic algorithm our model is compatible with larger datasets and unknown search space which is not possible in the baseline algorithm i.e. grid search. Moreover, after applying the genetic hyperparameter optimization technique, the overall accuracy, recall, and F1 score is increased for all vessel types significantly. While comparing our optimized model with the existing model with different evaluation metrics, our model’s performance is outstanding.
Volume: 37
Issue: 3
Page: 1626-1636
Publish at: 2025-03-01

Secure e-voting system using Schorr's zero-knowledge identification protocol

10.11591/csit.v6i1.p20-27
Indah Octaviani Laleb , Daniel M.D.U. Kasse
In today's era of technological progress, the electoral system has changed significantly with the introduction of electronic voting (e-voting). The traditional voting system poses many vulnerabilities to manipulation, potential human error, and problems with voter privacy. These limitations can lead to reduced trust and participation in elections. E-voting has emerged to address this issue, aiming to improve the convenience, security, and privacy of voters. E-voting systems are evaluated on accuracy, security, privacy, and transparency; however, ensuring voter privacy while maintaining these principles remains a significant challenge. A potential solution to improving privacy in e-voting is Schorr's zero-knowledge identification protocol. This protocol allows voters to confirm their identity without revealing personal information, maintaining voter privacy throughout the process. By implementing these protocols, the e-voting system can strengthen security and privacy, making elections more transparent and trustworthy. As technology evolves, adopting solutions like Schorr's zero-knowledge identification protocol can help e-voting systems meet the growing demand for safe, fair, and private elections.
Volume: 6
Issue: 1
Page: 20-27
Publish at: 2025-03-01

Advancements in seismic data collection and analysis through machine learning

10.11591/ijeecs.v37.i3.pp2058-2068
Sujata Kulkarni , Malay Phadke , Ashwini Sawant , Neel Patel , Om Patil
The evolution of seismic data collection has been driven by the need for stations to capture large volumes of high-frequency signals continuously. These signals typically contain both seismic and non-seismic information. Previous research converted SEED data into CSV format and used principal component analysis (PCA) for feature extraction from the seismic dataset. Machine learning models were then employed, showing an improvement in identifying seismic and non-seismic events. This paper focuses on applying deep learning methods, specifically deep neural networks (DNN) and a hybrid model combining long short-term memory (LSTM) networks with DNN (LSTM+DNN). The proposed deep learning models demonstrate a notable improvement over traditional machine learning technique. Experimental results show a test accuracy of 99.24% using deep learning, compared to an average of 97.80% achieved with machine learning models, indicating a 1.46% enhancement in detection accuracy. This underscores the potential of deep learning in accurately detecting seismic events in real-time monitoring systems.
Volume: 37
Issue: 3
Page: 2058-2068
Publish at: 2025-03-01

Attention deficit and hyperactivity disorder classification in quantitative EEG signals using machine learning algorithms

10.11591/ijeecs.v37.i3.pp1580-1587
Syifani Ihfadza Aliyah , Sastra Kusuma Wijaya , Yetty Ramli
Attention deficit and hyperactivity disorder (ADHD) classification method as a quantitative observation has been continually improved to assist medical practitioners. Currently, machine learning algorithms such as k-nearest neighbors (KNN), multilayer perceptron (MLP), and support vector machine (SVM) are widely used. This study proposed a feature extraction method for quantitative electroencephalography (qEEG) data derived from the continuous wavelet transform (CWT) to classify children with ADHD versus healthy subjects. Subsequently, this study compared the performance of the classification pipeline before and after the implementation of principal component analysis (PCA) on the features prior to processing with machine learning algorithms. The results revealed that the overall performance of the classifiers consistently improved after the implementation of PCA. The results highlight the varying impact of PCA on classifier performance, with KNN showing an improvement in testing accuracy from 61.84% to 69.21% following PCA implementation, while the other classifiers showed deterioration in performance. These findings suggest that while PCA may be beneficial for some classifiers, its impact on performance varies depending on the specific characteristics of the dataset and the classifier utilized. Moreover, this study provides insight for future implementation of the classification method for ADHD patients across a more specific clinical range of the spectrum.
Volume: 37
Issue: 3
Page: 1580-1587
Publish at: 2025-03-01

Field programmable gate array simulation and study on different multiplexer hardware for electronics and communication

10.11591/csit.v6i1.p28-39
Arvind Kumar , Adesh Kumar , Anurag Vijay Agrawal
Multiplexing is the technique of transmitting two or more separate signals concurrently using a single communication channel. Multiplexing enables the augmentation of communication channels and consequently the volume of data that may be transmitted. Communication networks utilize diverse multiplexing techniques. An input multiplexer amalgamates various network signals into a singular composite signal before transmission over a shared medium. The composite signal is broken back into its component signals by a demultiplexer, when it reaches its destination, allowing further operations to utilize them separately. The design of the hardware chip depends on the configuration of the multiplexer and demultiplexer in the communication system. The work is presented as a study of the digital logic design and simulation of the different configurations of the multiplexer hardware. The performance evaluation is carried out on the different series of Xilinx field programmable gate array (FPGA) such as Spartan-6, Spartan-3E, Virtex-5, and Virtex-6 with logically checked in Xilinx ISE waveform simulator software. The current analysis of the design and simulation of different configurations of the multiplexer design helps the designers to estimate the chip performance. The novelty of the work lies in its scalable and programmable architecture fitted for specific communication systems that assess performance based on latency, frequency, and power consumption that can be further linked with communication protocols.
Volume: 6
Issue: 1
Page: 28-39
Publish at: 2025-03-01

Leveraging 3D convolutional networks for effective video feature extraction in video summarization

10.11591/ijeecs.v37.i3.pp1616-1625
Bhakti Deepak Kadam , Ashwini Mangesh Deshpande
Video feature extraction is pivotal in video processing, as it encompasses the extraction of pertinent information from video data. This process enables a more streamlined representation, analysis, and comprehension of video content. Given its advantages, feature extraction has become a crucial step in numerous video understanding tasks. This study investigates the generation of video representations utilizing three-dimensional (3D) convolutional neural networks (CNNs) for the task of video summarization. The feature vectors are extracted from the video sequences using pretrained two-dimensional (2D) networks such as GoogleNet and ResNet, along with 3D networks like 3D Convolutional Network (C3D) and Two-Stream Inflated 3D Convolutional Network (I3D). To assess the effectiveness of video representations, F1-scores are computed with the generated 2D and 3D video representations for chosen generic and query-focused video summarization techniques. The experimental results show that using feature vectors from 3D networks improves F1-scores, highlighting the effectiveness of 3D networks in video representation. It is demonstrated that 3D networks, unlike 2D ones, incorporate the time dimension to capture spatiotemporal features, providing better temporal processing and offering comprehensive video representation.
Volume: 37
Issue: 3
Page: 1616-1625
Publish at: 2025-03-01

A hybrid approach for hotspot problem using load balancing and advanced ant colony algorithm

10.11591/ijeecs.v37.i3.pp1865-1873
Padmini Mysuru Srikantha , Sampath Kuzhalvaimozhi , Susheela Shimoga Balasubramanya , Sklita Buthello , Sadana Hulugundi Jagadish , Spandana Mallegowdanakoppalu Thammanna
Wireless sensor networks (WSNs) are crucial in various applications such as environmental surveillance, military operations, transportation monitoring, and healthcare. However, due to a finite set of sensor nodes' resources concerning energy, memory, disk, and CPU processing, nodes in WSNs often face hotspot issues. The sensor nodes that are located near the base station, are responsible for relaying data not only from themselves but also from neighboring nodes. This leads to hotspot issues, where nodes near the base station experience higher traffic loads and faster energy depletion. This paper mainly focuses on mitigating hotspot issues in heterogeneous WSNs using unequal clustering, load balancing, and an advanced ant colony algorithm. This approach involves devising strategies for selecting cluster heads, determining clusters optimal number and formation, and optimizing data transmission processes. Central to the methodology is utilizing load balancing mechanisms and an advanced ant colony algorithm to distribute the workload among sensor nodes more evenly and find the optimum routing path. The proposed algorithm shows promise in alleviating traffic congestion and energy depletion and provides an innovative approach to enhance network performance and prolong the lifespan of sensor nodes.
Volume: 37
Issue: 3
Page: 1865-1873
Publish at: 2025-03-01

Empower BreastNet: breast cancer detection with transfer learning VGG Net-19

10.11591/ijeecs.v37.i3.pp1927-1935
Vaishali M. Joshi , Prajkta P. Dandavate , Rashmi Ramamurthy , Riddhi Mirajkar , Neeta N. Thune , Gitanjali R. Shinde
Breast cancer is a major cause of death among women globally, making early detection crucial for effective treatment. This study introduces a new deep learning (DL) method using transfer learning (TL) to automatically detect and diagnose breast cancer. TL improves performance on new tasks by using knowledge from previous tasks. In this study, we use pre-trained convolutional neural networks (CNNs) like AlexNet, ResNet50, visual geometry group (VGG)-16, and VGG-19 to extract features from the breast cancer wisconsin (BCW) diagnostic dataset. We measure the model's success with accuracy, sensitivity, specificity, precision, and F-score. The results show that the VGG-19 model, when applied with TL, performs best for diagnosing breast cancer, achieving an overall accuracy of 98.75%, sensitivity of 97.38%, specificity of 98.35%, precision of 97.35%, and an F-score of 97.66%.
Volume: 37
Issue: 3
Page: 1927-1935
Publish at: 2025-03-01

Visual treatment with AR for children with dysphasia

10.11591/ijeecs.v37.i3.pp1702-1711
Misael Lazo Amado , Meyluz Monica Paico Campos
The language disorder known as dysphasia significantly affects the ability to communicate effectively, presenting challenges in both comprehension and expression of language. To address this issue, the development of a visual treatment using augmented reality (AR) specifically designed for children with dysphasia has been proposed. The methodology selected for this project is analysis, design, development, implementation, and evaluation (ADDIE), an innovative methodology that encompasses analysis, design, development, implementation and evaluation. This methodology is perfectly adapted to the needs of the project, allowing a systematic and complete approach at all stages of the process of creating the visual treatment. The results obtained show that the visual treatment with AR has been positively evaluated by development experts and dysphasia specialists. Its innovative capacity to assist children with this disorder in health and educational settings is highlighted. This approach provides an effective tool to improve the communication and language development of children affected by dysphasia, offering new opportunities for their learning and growth. Its implementation in healthcare and educational settings could have a significant impact on the quality of life and development of these children.
Volume: 37
Issue: 3
Page: 1702-1711
Publish at: 2025-03-01

Analysis of telehealth acceptance for basic life support training in sudden cardiac arrest in Pontianak

10.11591/csit.v6i1.p48-57
Ruhil Iswara , Sri Kusumadewi , Rahadian Kurniawan
Sudden cardiac arrest (SDA), which is one of the most prevalent causes of mortality, can be prevented by quickly conducting basic life support (BLS). In Pontianak City, the challenges associated with obtaining emergency health training, such as BLS, remain high. This study aims to evaluate user acceptance of telehealth as well as its effectiveness in BLS training. We will also discuss its impact on community knowledge and skills in managing cardiac arrest. We used the HOT-Fit method to analyze the level of acceptance of telehealth in BLS training. We collected data from 60 respondents who underwent telehealth-based BLS training. The results showed that participants' understanding and readiness in dealing with heart attack emergencies had increased significantly, by 90% and 92%, respectively. Analysis of the level of acceptance with HOT-Fit showed that system quality had the greatest influence on system use (0.611). Service quality exerted the most significant impact on user satisfaction (0.568). The net benefit was influenced by system use, user satisfaction, and organizational support, with user satisfaction having the greatest influence (0.600). Further research will be conducted on the utilization of augmented reality (AR) or virtual reality (VR) technology to implement telehealth for BLS training.
Volume: 6
Issue: 1
Page: 48-57
Publish at: 2025-03-01

Matrix inversion using multiple-input multiple-output adaptive filtering

10.11591/csit.v6i1.p1-7
Muhammad Yasir Siddique Anjum , Javed Iqbal
A new approach for matrix inversion is introduced. The approach is based on vector representation of multiple-input multiple-output (MIMO) channel matrix, in which the channel matrix is described by a linear combination of channel vectors weighted by their respective system inputs. The MIMO system output is then fed into a bank of adaptive filters, wherein the response of a given adaptive filter is iteratively minimized to match its output to the given system input. In doing so, adaptive filters equalize the impact of respective channel vectors on the MIMO channel output, while simultaneously orthogonalizing themselves from all other channel vectors, forming the channel matrix inverse. The method demonstrates satisfactory convergence and accuracy in Monte Carlo simulations conducted with varying signal-to-noise ratios (SNRs) and matrix conditioning scenarios. The suggested approach, by virtue of its adaptable characteristics, can also be employed for time-varying linear equation systems.
Volume: 6
Issue: 1
Page: 1-7
Publish at: 2025-03-01
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