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IAES International Journal of Artificial Intelligence (IJ-AI)
Published by Institute of Advanced Engineering and Science
ISSN : 20894872     EISSN : 22528938     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; etc.
Arjuna Subject : -
Articles 1,808 Documents
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Machine learning-based decision-making approach for predicting defects detection: a case study Barzizza, Elena; Biasetton, Nicolo; Ceccato, Riccardo; Molena, Alberto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3052-3060

Abstract

In today’s highly competitive global market, industries must produce faultless products to achieve profitability. Machine learning (ML) algorithms provide a possible method to improve quality standards by enabling the prediction of the outcome of quality control processes. This article presents a real case study based on ML algorithms suggested to develop a knowledge-based intelligent supervisory system to predict defect products in the fashion industry. Defect detection is formulated as a binary classification problem, and several ML algorithms have been compared to determine the most suitable one on the available data. The random forest (RF), LightGBM, and C5.0 algorithms exhibit comparable high-end performances on the pre-processed dataset made available by the company. Nevertheless, since the aim of the analysed industry is to reduce the rate of false negative observations (i.e., the proportion of defected-free products wrongly classified), the best method results is RF, as it minimizes this metric.
Predicting psycho-somatic disorders in online activity using multi-layer perceptron Gadiparthi, Manjunath; Reddy, Edara Srinivasa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp687-694

Abstract

Internet services such as social media, blogs, and websites make it possible for people to acquire knowledge instantly. Due to these websites, it is now considerably easier to communicate information. As a result, individuals increasingly devote a higher amount of time to social networking programmes. This study provides estimates about the potential future ramifications of how individuals will utilise social networks. This work presents an accurate and applicable model for forecasting undesirable consequences. The model is of sufficient quality to be useful. This has been the case throughout. Using the model that has been proposed, significant properties are identified from datasets. After recovering the properties, they are categorised using the complicated computational method of multi-layer perceptron-based (MLP) artificial neural networks (ANN). 70% of this data was utilised during the training phase of the machine learning algorithm, while the remaining 30% was utilised during the validation phase of model construction. The proposed model's results were compared to those of more standard machine learning techniques. The approach utilises social networks to predict the issue. The simulation results indicate that the suggested model generates more precise predictions than the support vector machine, logistic regression, and random forest decision tree classifier techniques combined.
Breast cancer detection through attention based feature integration model Guptha, Sharada; Eshwarappa, Murundi N
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2254-2264

Abstract

Breast cancer is detected by screening mammography wherein X-rays are used to produce images of the breast. Mammograms for screening can detect breast cancer early. This research focuses on the challenges of using multi-view mammography to diagnose breast cancer. By examining numerous perspectives of an image, an attention-based feature-integration mechanism (AFIM) model that concentrates on local abnormal areas associated with cancer and displays the essential features considered for evaluation, analyzing cross-view data. This is segmented into two views the bi-lateral attention module (BAM) module integrates the left and right activation maps for a similar projection is used to create a spatial attention map that highlights the impact of asymmetries. Here the module's focus is on data gathering through medio-lateral oblique (MLO) and bilateral craniocaudal (CC) for each breast to develop an attention module. The proposed AFIM model generates using spatial attention maps obtained from the identical image through other breasts to identify bilaterally uneven areas and class activation map (CAM) generated from two similar breast images to emphasize the feature channels connected to a single lesion in a breast. AFIM model may easily be included in ResNet-style architectures to develop multi-view classification models.
Anamoly based intrusion detection using ensemble machine learning and block-chain Mekala, Srinivasa Rao; Nazma, Shaik; Nava Chaitanya, Kumbhagiri; Ambica, Thota
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2754-2762

Abstract

A major issue facing the quickly evolving technological world is the surge in security concerns, particularly for critical Internet-of-Things (IoT) applications like health care and the military. Early security attack detection is crucial for safeguarding important resources. Our research focuses on developing an anomaly-based intrusion detection system (IDS) using machine learning (ML) models. With the use of voting strategies, Bagging Ensemble, Boosting Ensemble, and Random Forest, we created a robust and long-lasting IDS. The F1 score is a crucial metric for measuring accurate predictions at the class level and serves as the focus of these ML systems. Maintaining a high F1 score in critical applications highlights the constant need for development. Make use of the latest CICIoT2023 data-set employ Hyper-ledger Fabric to create a private channel in order to bolster the security of our IDS through the usage of block-chain technology. We use block-chain's immutable record and cryptographic techniques to establish a decentralized, tamper-proof environment. Consequently, our proposed approach provides an efficient intrusion detection system that significantly enhances resource protection and alerting the user in prior with intruder information   incritical regions for Internet of Things security applications.
Enhancements in the world of digital forensics Vaddi, Krishna Sanjay; Kamble, Dhwaniket; Vaingankar, Raj; Khatri, Tushar; Bhalerao, Pranil
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp680-686

Abstract

Currently, the rapid advancement of computer systems and mobile phones has resulted in their utilization in unlawful acts. Ensuring adequate and effective security measures poses a difficult task due to the intricate nature of these devices, thereby exacerbating the challenges associated with investigating crimes involving them. Digital forensics, which involves investigating cyber crimes, plays a crucial role in this realm. Extensive research has been conducted in this field to aid forensic investigations in addressing contemporary obstacles. This paper aims to explore the progress made in the applications of digital forensics and security, encompassing various aspects, and provide insights into the evolution of digital forensics over the past five years.
Comparing logistic regression and extreme gradient boosting on student arguments Wahyuningsih, Tri; Manongga, Danny; Sembiring, Irwan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3119-3128

Abstract

Identifying the effectiveness level and quality of students' arguments poses a challenge for teachers. This is due to the lack of techniques that can accurately assist in identifying the effectiveness and quality of students' arguments. This research aims to develop a model that can identify effectiveness categories in students' arguments. The method employed involves the logistic regression+XGBoost algorithm combined with separate implementations of term frequency-inverse document frequency (TF-IDF) and CountVectorizer. Student argument data were collected and processed using natural language processing techniques. The research results indicate that TF-IDF outperforms in identifying effectiveness classes in student arguments with an accuracy of 66.20%. The multi-output classification yielded an accuracy of 89.32% in the initial testing, which further improved to 92.34% after implementing one-hot encoding. A novel finding in this research is the superiority of TF-IDF as a technique for identifying effectiveness classes in student arguments compared to CountVectorizer. The implications of this research include the development of a model that can assist teachers in identifying the effectiveness level of students' arguments, thereby improving the quality of learning and enhancing students' argumentative competence.
K-centroid convergence clustering identification in one-label per type for disease prediction Hoang, Minh Long; Delmonte, Nicola
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp1149-1159

Abstract

Disease prediction is a high demand field which requires significant support from machine learning (ML) to enhance the result efficiency. The research works on application of K-means clustering supervised classification in disease prediction where each class only has one labeled data. The K-centroid convergence clustering identification (KC3I) system is based on semi-K-means clustering but only requires single labeled data per class for the training process with the training dataset to update the centroid. The KC3I model also includes a dictionary box to index all the input centroids before and after the updating process. Each centroid matches with a corresponding label inside this box. After the training process, each time the input features arrive, the trained centroid will put them to its cluster depending on the Euclidean distance, then convert them into the specific class name, which is coherent to that centroid index. Two validation stages were carried out and accomplished the expectation in terms of precision, recall, F1-score, and absolute accuracy. The last part demonstrates the possibility of feature reduction by selecting the most crucial feature with the extra tree classifier method. Total data are fed into the KC3I system with the most important features and remain the same accuracy.
Advancing machine learning for identifying cardiovascular disease via granular computing Ku Khalif, Ku Muhammad Naim; Muhammad, Noryanti; Mohd Aziz, Mohd Khairul Bazli; Irawan, Mohammad Isa; Iqbal, Mohammad; Setiawan, Muhammad Nanda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2433-2440

Abstract

Machine learning in cardiovascular disease (CVD) has broad applications in healthcare, automatically identifying hidden patterns in vast data without human intervention. Early-stage cardiovascular illness can benefit from machine learning models in drug selection. The integration of granular computing, specifically z-numbers, with machine learning algorithms, is suggested for CVD identification. Granular computing enables handling unpredictable and imprecise situations, akin to human cognitive abilities. Machine learning algorithms such as Naïve Bayes, k-nearest neighbor, random forest, and gradient boosting are commonly used in constructing these models. Experimental findings indicate that incorporating granular computing into machine learning models enhances the ability to represent uncertainty and improves accuracy in CVD detection.
Efficient fault tolerant cost optimized approach for scientific workflow via optimal replication technique within cloud computing ecosystem Anjum, Asma; Parveen, Asma
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp122-132

Abstract

Cloud computing is one of the dispersed and effective computing models, which offers tremendous opportunity to address scientific issues with big scale characteristics. Despite having such a dynamic computing paradigm, it faces several difficulties and falls short of meeting the necessary quality of services (QoS) standards. For sustainable cloud computing workflow, QoS is very much required and need to be addressed. Recent studies looked on quantitative fault-tolerant programming to reduce the number of copies while still achieving the reliability necessity of a process on the heterogeneous infrastructure as a service (IaaS) cloud. In this study, we create an optimal replication technique (ORT) about fault tolerance as well as cost-driven mechanism and this is known as optimal replication technique with fault tolerance and cost minimization (ORT-FTC). Here ORT-FTC employs an iterative-based method that chooses the virtual machine and its copies that have the shortest makespan in the situation of specific tasks. By creating test cases, ORT-FTC is tested while taking into account scientific workflows like CyberShake, laser interferometer gravitational-wave observatory (LIGO), montage, and sipht. Additionally, ORT-FTC is shown to be only slightly improved over the current model in all cases. 
Extracting features of tomato viral leaf diseases using image processing techniques Sagar, Sanjeela; Singh, Jaswinder
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp925-932

Abstract

Agriculture is the main livelihood of Indians. More than 50% of Indian population Is dependent on it and it contributes about 18% of Indian gross domestic product (GDP). According to Inc42, the agricultural sector of India is predicted to increase to US$ 24 billion by 2025. With the increase in population, the demand for food also increases, but more than 30% of crops get affected due to crop diseases. Overall, India lost approximately five million hectares of crop area to flash floods, cyclonic storms, floods, cloudbursts, and landslides till 2021. In that case, there is a need to prevent crops from diseases to fulfil demand supply ratio. This paper presents the feature extraction of tomato viral leaf diseases using various image processing techniques. Most of the research uses Convolutional Neural networks to extract the features of these diseases, but these neural networks are not performing much accurately in real scenarios, so there is a need to extract the features using image processing methods. During the study, it is found that these diseases have different colours, shapes and textures and these features can be used with convolution neural networks to bring more accurate results in real scenarios.

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