IAES International Journal of Artificial Intelligence (IJ-AI)
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.
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<![CDATA[An efficient security analysis of bring your own device ]]>
<![CDATA[Pullagura Soubhagyalakshmi]]>;
<![CDATA[Kalli Satyanarayan Reddy]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v12.i2.pp696-703
<![CDATA[The significant enhancement in demand for bring your own device (BYOD) mechanism in several organizations has sought the attention of several researchers in recent years. However, the utilization of BYOD comes with a high risk of losing crucial information due to lesser organizational control on employee-owned devices. The purpose of this article is to review and analyze the various security threats in BYOD; further we review the existing work that was developed in order to reduce the risks present in BYOD. A detailed review is presented to detect BYOD security threats and their respective security policies. A phase-by-phase mitigation strategy is developed based on the components and crucial elements identified using review policy. Managerial-level, social-level and technical level issues are identified such as illegal access, leaking delicate company data, lower flexibility, corporate data breaching, and employee privacy. It is analyzed that collaboration of people, security policy factors and technology in an effective manner can mitigate security threats present in the BYOD mechanism. This article initiates a move towards filling the security gap present the BYOD mechanism. This article can be utilized for providing guidelines in various organizations. Ultimately, successful implementation of BYOD depends upon the balance created between usability and security.]]>
<![CDATA[Evaluation of massive multiple-input multiple-output communication performance under a proposed improved minimum mean squared error precoding ]]>
<![CDATA[Dheyaa Jasim Kadhim]]>;
<![CDATA[Muna Hadi Saleh]]>;
<![CDATA[Sadiq Jassim Abou-Loukh]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v12.i2.pp984-994
<![CDATA[The fundamental of a downlink massive multiple-input multiple-output (MIMO) energy- issue efficiency strategy is known as minimum mean squared error (MMSE) implementation degrades the performance of a downlink massive MIMO energy-efficiency scheme, so some improvements are adding for this precoding scheme to improve its workthat is called our proposal solution as a proposed improved MMSE precoder (PIMP). The energy efficiency (EE) study has also taken into mind drastically lowering radiated power while maintaining high throughput and minimizing interference issues. We further find the tradeoff between spectral efficiency (SE) and EE although they coincide at the beginning but later their interests become conflicting and divergent then leading EE to decrease so gradually while SE continues increasing logarithmically. The results achieved that for a single-cellular massive MU-MIMO downlink model, our PIMP scheme is the appropriate scenario to achieve higher precoding performance system. Furthermore, both maximum ratio transmission (MRT) and PIMP are suitable for performance improvement in massive MIMO results of EE and SE. So, the main contribution comes with this work that highest EE and SE are belong to use a PIMP which performs better appreciably than MRT at bigger ratio of number of antennas to the number of the users. ]]>
<![CDATA[BMSP-ML: big mart sales prediction using different machine learning techniques ]]>
<![CDATA[Rao Faizan Ali]]>;
<![CDATA[Amgad Muneer]]>;
<![CDATA[Ahmed Almaghthawi]]>;
<![CDATA[Amal Alghamdi]]>;
<![CDATA[Suliman Mohamed Fati]]>;
<![CDATA[Ebrahim Abdulwasea Abdullah Ghaleb]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v12.i2.pp874-883
<![CDATA[Variations in sales over time is the main issue faced by many retailers. To overcome this problem, we attempt to predict the sales by comparing the previous sales data of different stores. Firstly, the primary task is to recognize the pattern of the factors that help to predict sales. This study helps us understand the data and predict sales using many machines learning models. This process gets the data and beautifies the data by imputing the missing values and feature engineering. While solving this problem, predicting the monthly sales value is significant in the study. In addition, an essential element is to clear the missing data and perform proper feature engineering to better understand them before applying them. The experimental results show that the random forest predictor has outperformed ridge regression, linear regression, and decision tree models among the four machine learning techniques implemented in this study. The performance of the proposed models has been evaluated using root mean square error (RMSE).]]>
<![CDATA[Robustness enhancement study of augmented positive identification controller by a sigmoid function ]]>
<![CDATA[Abbas H. Issa]]>;
<![CDATA[Sarab A. Mahmood]]>;
<![CDATA[Abdulrahim T. Humod]]>;
<![CDATA[Nihad M. Ameen]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v12.i2.pp686-695
<![CDATA[The dissolved oxygen concentration in the wastewater treatment process (WWTP) must remain in a specific range while the factory operates. The augmented positive identification (PID) controller with a nonlinear element (sigmoid function) is proposed to assure stability and reduce uncertainties in the wastewater direct reuse/recycling model. The nonlinear controller gains (PID controller with sigmoid function) for uncertain wastewater treatment processes are tuned using the particle swarm optimization (PSO) technique. The proposed robust method for controlling wastewater treatment processes has good robustness during model mismatching, reduces treatment time compared to traditional positive identification (PID) controllers tuned by PSO, is easy to apply, and has good performance, according to simulation results.]]>
<![CDATA[Effect of word embedding vector dimensionality on sentiment analysis through short and long texts ]]>
<![CDATA[Mohamed Chiny]]>;
<![CDATA[Marouane Chihab]]>;
<![CDATA[Abdelkarim Ait Lahcen]]>;
<![CDATA[Omar Bencharef]]>;
<![CDATA[Younes Chihab]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v12.i2.pp823-830
<![CDATA[Word embedding has become the most popular method of lexical description in a given context in the natural language processing domain, especially through the word to vector (Word2Vec) and global vectors (GloVe) implementations. Since GloVe is a pre-trained model that provides access to word mapping vectors on many dimensionalities, a large number of applications rely on its prowess, especially in the field of sentiment analysis. However, in the literature, we found that in many cases, GloVe is implemented with arbitrary dimensionalities (often 300d) regardless of the length of the text to be analyzed. In this work, we conducted a study that identifies the effect of the dimensionality of word embedding mapping vectors on short and long texts in a sentiment analysis context. The results suggest that as the dimensionality of the vectors increases, the performance metrics of the model also increase for long texts. In contrast, for short texts, we recorded a threshold at which dimensionality does not matter.]]>
<![CDATA[Predicting students’ academic performance using e-learning logs ]]>
<![CDATA[Malak Abdullah]]>;
<![CDATA[Mahmoud Al-Ayyoub]]>;
<![CDATA[Farah Shatnawi]]>;
<![CDATA[Saif Rawashdeh]]>;
<![CDATA[Rob Abbott]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v12.i2.pp831-839
<![CDATA[The outbreak of coronavirus disease 2019 (COVID-19) drives most higher education systems in many countries to stop face-to-face learning. Accordingly, many universities, including Jordan University of Science and Technology (JUST), changed the teaching method from face-to-face education to electronic learning from a distance. This research paper investigated the impact of the e-learning experience on the students during the spring semester of 2020 at JUST. It also explored how to predict students’ academic performances using e-learning data. Consequently, we collected students’ datasets from two resources: the center for e-learning and open educational resources and the admission and registration unit at the university. Five courses in the spring semester of 2020 were targeted. In addition, four regression machine learning algorithms had been used in this study to generate the predictions: random forest (RF), Bayesian ridge (BR), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). The results showed that the ensemble model for RF and XGBoost yielded the best performance. Finally, it is worth mentioning that among all the e-learning components and events, quiz events had a significant impact on predicting the student’s academic performance. Moreover, the paper shows that the activities between weeks 9 and 12 influenced students’ performances during the semester.]]>
<![CDATA[Query expansion based on modified Concept2vec model using resource description framework knowledge graphs ]]>
<![CDATA[Sarah Dahir]]>;
<![CDATA[Abderrahim El Qadi]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v12.i2.pp755-764
<![CDATA[The enormous size of the web and the vagueness of the terms used to formulate queries still pose a huge problem in achieving user satisfaction. To solve this problem, queries need to be disambiguated based on their context. One well-known technique for enhancing the effectiveness of information retrieval (IR) is query expansion (QE). It reformulates the initial query by adding similar terms that help in retrieving more relevant results. In this paper, we propose a new QE semantic approach based on the modified Concept2vec model using linked data. The novelty of our work is the use of query-dependent linked data from DBpedia as training data for the Concept2vec skip-gram model. We considered only the top feedback documents, and we did not use them directly to generate embeddings; we used their interlinked data instead. Also, we used the linked data attributes that have a long value, e.g., “dbo: abstract”, as training data for neural network models, and, we extracted from them the valuable concepts for QE. Our experiments on the Associated Press collection dataset showed that retrieval effectiveness can be much improved when a skip-gram model is used along with a DBpedia feature. Also, we demonstrated significant improvements compared to other approaches.]]>
<![CDATA[Spectrum sensing using 16-QAM and 32-QAM modulation techniques at different signal-to-noise ratio: a performance analysis ]]>
<![CDATA[Neha Chaudhary]]>;
<![CDATA[Rashima Mahajan]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v12.i2.pp966-973
<![CDATA[Spectrum sensing techniques are implemented for effective use of spectrum resources in the cognitive-radio. In this research work attempt has been made for the performance of energy detector with cooperative spectrum sensing using double dynamic threshold on the MATLAB software. Additive white gaussian noise is used and the frequency range between 54 MHz to 862 MHz is considered with wideband. Findings in receiver operative curve have been observed to analyze probability-of-detection (????????) under different values of probability-of-false alarm (????????) condition and diverse ranges of signal to noise ratio with different number of samples of input signal. Presence and absence of primary user has been marked by using a hypothetical model based on Neyman pearson approach. From the results, it has been observed that more the number of sample values, better is the probability-of-detection (????????) value as achieved for 32-QAM signal as compared to 16-QAM signal. Also, better results have been witnessed at -9db signal to noise ratio value as compared to -15db and -20db. This work provides almost 10% of enhancement in the probability of detection at -9db signal to noise ratio for 16-QAM modulated signal as compared to the existing model where the implementation of energy detector spectrum sensing technique through simulink model.]]>
<![CDATA[Deep learning speech recognition for residential assistant robot ]]>
<![CDATA[Robinson Jiménez-Moreno]]>;
<![CDATA[Ricardo A. Castillo]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v12.i2.pp585-592
<![CDATA[This work presents the design and validation of a voice assistant to command robotic tasks in a residential environment, as a support for people who require isolation or support due to body motor problems. The preprocessing of a database of 3600 audios of 8 different categories of words like “paper”, “glass” or “robot”, that allow to conform commands such as "carry paper" or "bring medicine", obtaining a matrix array of Mel frequencies and its derivatives, as inputs to a convolutional neural network that presents an accuracy of 96.9% in the discrimination of the categories. The command recognition tests involve recognizing groups of three words starting with "robot", for example, "robot bring glass", and allow identifying 8 different actions per voice command, with an accuracy of 88.75%.]]>
<![CDATA[Product defect detection based on convolutional autoencoder and one-class classification ]]>
<![CDATA[Meryem Chaabi]]>;
<![CDATA[Mohamed Hamlich]]>;
<![CDATA[Moncef Garouani]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v12.i2.pp912-920
<![CDATA[To meet customer expectations and remain competitive, industrials try constantly to improve their quality control systems. There is hence increasing demand for adopting automatic defect detection solutions. However, the biggest issue in addressing such systems is the imbalanced aspect of industrial datasets. Often, defect-free samples far exceed the defected ones, due to continuous improvement approaches adopted by manufacturing companies. In this sense, we propose an automatic defect detection system based on one-class classification (OCC) since it involves only normal samples during training. It consists of three sub-models, first, a convolutional autoencoder serves as latent features extractor, the extracted features vectors are subsequently fed into the dimensionality reduction process by performing principal component analysis (PCA), then the reduced-dimensional data are used to train the one-class classifier support vector data description (SVDD). During the test phase, both normal and defected images are used. The first two stages of the trained model generate a low-dimensional features vector, whereas the SVDD classifies the new input, whether it is defect-free or defected. This approach is evaluated on the carpet images from the industrial inspection dataset MVTec anomaly detection (MVTec AD). During training, only normal images were used. The results showed that the proposed method outperforms the state-of-the-art methods.]]>
