1.Introduction

Online social media is now a part of everyday life activity; without a digital footprint, it has become increasingly difficult to survive in this new age of digital media. Cyberbullying is defined as an electronic form of intentional harm and hate to someone and it’s considered as a crime [1]. The work presented in [2]  reports that cyberbullying had a major and long-term impact on the victims. Cyberbullying leaves both the abuser (predator) and the victim with mental and physical consequences. Different researchers [2], [3] have reported that victims attempted suicide due to many cyberbullying incidents, where they have been mentally abused by offensive and violent messages received from abusers. Numerous studies have shown that adolescents are the primary victims [3], [4]. Despite the regulations, presented in most of the countries, that protect and help bullying victims, there are still many people who suffer from this phenomenon. Indeed, if the victim or his family doesn’t report the case of bullying, the victim will keep suffering, and the abuser will continue making other victims. Therefore, the early detection of bullying will help in finding an effective solution by protecting the targeted person and punishing the abuser. Measures to track and identify potentially harmful online behavior must therefore be implemented. Because of the large number of daily posts, and the huge amount of information that circulates through the different social media platforms, manual checking for all posts is just impossible. Consequently, several studies focused on finding a way to autodetect the presence of cyberbullying quickly and effectively in order to avoid any serious consequences [5], [6].

In this paper, we present an exhaustive list of the most recent research dedicated to autodetecting cyberbullying by focusing mainly on machine learning, neuronal networks and deep learning techniques. We undertook a deep review of evaluation methods, features, dataset size, language, and dataset source of the latest research in this field. In the following section we give the reader a background of cyberbullying, followed by section 3 where we discuss and present the cyberbullying approaches based or website is denigrated.

To accomplish the primary objective of this study, we identified our research questions as follows:

• RQ1: Which dataset was mainly used for the classification of cyberbullying?
• RQ2: What was the size and language of the dataset?
• RQ3: What was the method of classification used? And which one has been used most?
• RQ4: What were the metrics of quality used?
• RQ5: Which approach proved the most effective?
• RQ6: Which features were the most frequently used with classifiers?

2. Research Method

The main stages of the review methodology (research strategy, quality evaluation criteria) were outlined in this section, as in Figure 1.

2.1. Data Sources

In January 2020, we started this study, and we included most studies from 2016 to 2019 and some studies before that. We used the following databases: IEEE, Science Direct, ACM.

2.2. Inclusion and exclusion criteria

The inclusion and exclusion criteria were developed in the selection criteria stage to ensure that the research included in this study was valuable and relevant and would lead us to our main objective.

Inclusion criteria

• Papers that are purely for the classification of cyberbullying in texts.
• Papers that used neural networks / machine learning in the classification.
• Papers that explained the model and its performance measures.
• Papers which mentioned the size and language of the dataset.

Exclusion criteria

• Papers not for text classification
• Papers that didn’t mention the findings’ accuracy.
• Paper had not been published in journal or conference
• Paper did not use neural networks / machine learning in the classification.
• Paper that didn’t mention the size and language of the dataset.

Figure 1: Data Sources

2.3. Search strategy

We used the bellow keywords to collect all the previous studies:

• “Cyberbullying” and “classification” and “neural networks” and “text”
• “Cyberbullying” and “classification” and “deep learning” and “text”
• “Cyberbullying” and “detection” and “deep learning” and “text”
• “Cyberbullying” and “classification” and “machine learning”
• “Cyberbullying” and “categorization” and “text”
• “Cyberbullying” and “classification” and “text”

2.4. Quality assessment evaluation

In this part, we designed quality assessment questions to make a checklist for the research and ensure that it would satisfy the aim of this systematic review.

• Q1: Was the corpus (size and language) identified and described well?
• Q2: Were the text classification approach described clearly?
• Q3: Was the performance of the method identified clearly?

3. Cyberbullying Background

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Cyberbullying is defined as the use of communication technology and information such as messages, photographs or videos in order to spread aggressive actions with the intention of harming others [1]. Unlike, bullying, cyberbullying does not require the presence of the victim in the same place or near the bully’s place [7].Therefore, it differs from traditional bullying that depends on direct abuse towards victims who could be children, adolescents, or women through physical aggression and intentional, visible behavior [8]. Due to the development of technology and the increase of using smart devices, cyberbullying has become more common and represent a real problem, nothing seems to be able to stop. This is because it is done via the Internet, which involves unknown distances and sources, that allow users to speak without restrictions and it is easy to repeat the aggressive actions at any time and could spread rapidly. Cyberbullying it’s not a new problem, in 2005, [2], indicated that the correlation between bullying and psychological symptoms is a reality. These symptoms may compromise risk factors involved in psychopathology. The authors indicate that bullying causes violence, delinquency, depression, anxiety, self-destructive, identity and suicidal issues, and that such symptoms could lead to psychopathology. Cyberbullying’s effects are profound and could have major and long-term effects compared to traditional bullying, especially for teenagers who present the largest proportion of victims. According to statistics of [3], [4], several victims of cyberbullying tried to commit suicide because of the, degrading, and violent texts that abusers sent to them .

3.1.  Cyberbullying Categories

There are several categories of cyberbullying as stated in [9], [10].

• Flooding: Consists of the bully giving the same one regularly comments, nonsense comments, or even by clicking on the enter key, in order not to let the victim contribute to the conversation.
• Masquerade: includes logging in to bully in a forum, chat room, or software using the account name of another person in order to bully a victim or tarnish the image of the victim.
• Flaming (bashing): involves two or more users attacking each other on a personal level. The conversation consists of a heated, short lived argument, and there is bullying language in all of the users’ posts.
• Trolling (baiting): is intentionally posting opinion agreeing with other posts in an emotionally loaded thread in order to provoke a war, even though the comments do not actually reflect the actual opinion of the poster.
• Harassment: resembles conventional bullying most closely with the assumed relationship of the bully and victim. This form of cyberbullying involves sending the victim repeatedly abusive messages over extended periods of time.
• Cyberstalking and cyberthreats: involve sending messages that include harm attacks, which are threatening or extremely aggressive, or include extortion.
• Denigration: this kind of bullying includes Writing obscene, negative, or false rumors of someone to others or sharing them on a public forum or chat room, or website.
• Outing: this kind of bullying includes posting confidential, personal, or embarrassing information in a public chat room or forum. This type of bullying is close to denigration but requires the abuser and the victim to have a close relationship with each other either online or in person.
• Exclusion: this type of cyberbullying occurred most frequently in chat rooms or conversations between young people and adolescents by ignoring the victim.

3.2. Cyberbullying prevention methods and limitations

Due to the extreme worldwide prevalence of cyberbullying and it’s direct link with many negative psychological symptoms, researchers have  studied the relationship of cyberbullying with several factors in order to help in  cyberbullying prevention. The work presented in [11], found that supplying sympathy and strengthening the relationship between caregiver and adolescents affect positively in cyberbullying prevention. On the other hand , the work that represented in [12], reports that  the improving of awareness on cyberbullying issues  played an  important role in cyberbullying prevention. In addition, some countries considered the cyberbullying as a crime. Therefore, they put the laws in dealing with anyone doing such a crime as well as encouraged people to report any case of bullying, as in UAE. This is all to try to prevent children and teens from engaging in cyberbullying as well as to assist cyber victims to deal with the adverse effects of cyberbullying. Although, methods and tools continue to enhanced in cyberbullying detection , the access restrictions on high-quality data limit the applicability of state-of-the-art techniques. Consequently, much of the recent research uses small, heterogeneous datasets, without a thorough evaluation of applicability [13].

3.3. Cyberbullying automatic detection methods

The effective solution to detect bullying in the posts over social media is to build machine-based automated systems. It’s for categorizing information and producing reports where cyberbullying is detected so that with fewer losses all recorded incidents can be quickly sorted out and addressed. Different methods are used to detect cyberbullying such as machine learning techniques, Natural Language Processing (NLP), and Deep Learning (DL). Examples include in [14], several NLP models such as Bag of Words (BoW), Latent Semantic Analysis (LSA) and Latent Dirichlet Allotment (LDA) used to detect bullying in Social Networks. On the other hand , some of the autodetection methods were based on word embedding, which expands a list of predefined offensive terms and assigns different weights to obtain bullying and latent characteristics [15].

4. Cyberbullying Detection Approaches Language-based

In this section, we present the most relevant works conducted in cyberbullying detection. We organized them based on the selected language, either Arabic or Latin, and both the features and the classification used in both cases.

4.1. Detection in Arabic language

Alakrot et al in [6], investigated of using word-level and N-gram features with common pre-processing methods, including extra normalization effect on the efficiency of a trained SVM classifier .This is to detect offensive comments. Authors created dataset of 15,050 comments by collecting comments about the famous Arabic people from the YouTube platform. The dataset is available for public.As a result the stemming with pre-processing enhanced the detection of offensive language in casual Arabic text. In addition, the use of N-gram features increased the classifier efficiency. Despite the combination of stemming and N-gram features showed a negative impact on precision and recall ,pre-processing with stemming and N-grams (1-5) achieved the best performance as highlighted in Table 1.

Authors in [16], authors represented the first study in utilizing deep learning in Arabic cyberbullying detection .They utilized the same dataset in [17], with little changes. Changes include removing all hyperlinks, un-Arabic characters and emoticons. The dataset was tokenized into words to remove all unneeded characters before building the model’s layers. Word Embedding was created after that. The dataset is divided into 80% for training and 20% for testing, then they trained a Feed Forward Neural Network FFNN. Authors considered the final decision of the accuracy  93.33 percent with validation accuracy 94.27, for the seven-layer network. Although it achieved an accuracy 94.56% with the three hidden layers.

In [17],  it is suggested a system for detecting cyberbullying in English and Arabic text. The only features included in the first stage were text (content of tweet) and language (English, Arabic). In the second stage, authors used an affective tweets package, specifically the TweetToSentiStrength Feature Vector filter. SentiStrength used for weighting the tweets, (2 to 5) for positive feelings and (-2 to -5) for negative feelings ,(1 , -1) for representing neutral feelings. The English lexicon files were used by SentiStrength where subsequently replaced by custom-built Arabic files including weighted profane words .Haidar et al, built two datasets. First one was obtained from Facebook which reached 0.98GB of size in order to verify the system. Second one was collected from twitter to train and test the system. The Arabic dataset was collected from different dialects Lebanon, Syria, Gulf Area and Egypt mainly, it contains 35273 unique tweets after removing all duplicates. Authors show that there is a difference between the “yes” precisions between the two classifiers. In terms of precision, SVM was much higher with  “yes” class. However, the overall system precision was 93.4 for SVM, 90.1 for NB.

A study by [18], utilized a collection of predefined obscene words as seeds words to collect another list from a large set of 175 million tweets . These were used to create a list of obscene words to detect offensive language and hate speech. Authors then generated new list from 3,430 words by performing Log Odd Ratio (LOR) method on unigrams and bigrams features. Authors evaluated the detection of offensive language by using five methods which are (seeds Words (SW),SW+LOR (unigram),SW+ LOR (bigram), LOR (unigram), LOR (bigram). The highest precision achieved from using the list generated by LOR(unigram).Authors have made  the dataset public for research as well as the list of obscene words and hashtags.

Table 1 provides a comparative summary between the different approaches in Arabic cyberbullying detection discussed above.

4.2. Detection in Latin language

In [19], authors used sentiment analysis to detect instances of bullying in the social network using Naïve Bays classifier (NB). Authors worked on a balanced dataset consisting of 5000 English tweets. Authors collected messages that contained one of these words “Gay,” “Homo,” “Dike,” and “Queer”. For training data, queer word used to classify the positive tweets while the presence of any of these terms “Gay,” “Homo,” “Dike” used to classify the negative tweets. As a result, NB classifier achieved accuracy 67.3%.

Table 1: Comparative summary between the different approaches in Arabic cyberbullying detection

Table 2: Comparative summary between the different approaches in Latin cyberbullying detection

The authors in [20] , used an unsupervised approach for detecting cyberbully traces over  social platforms. This is by utilizing Growing Hierarchical Self-Organizing Map. The suggested model is based on machine learning (decision tree C4.5, SVM, NB) as well as techniques derived from NLP (Natural Language Processing) such as semantic, syntactic features of textual sentences. Authors in this work tested the proposed model in three different datasets and platforms. The performance range for each classifier is covered in table 2.

The authors in [21], adopted a supervised approach for cyberbullying detection. Authors used different machine learning classifiers, TFIDF and sentiment analysis algorithms for features extraction. The classifications were evaluated on different n-gram language models. Authors found out, neural network with 3-grams achieved higher accuracy 92.8% compared to SVM with 4- grams that achieved 90.3%. Furthermore, NN exceeded other classifiers on the same dataset in another work. The dataset obtained from Kaggle (Formspring.me) and consists of 1608 English instance conversations, labeled under two classes (Cyberbullying, non-Cyberbullying). Each class consists of 804 instances. The performance average rate for each classifier is highlighted in table 2.

A study by [22], authors adopted a supervised approach to the identification of bullying and harassment in posted messages in Turkish language. Authors used information gain and chi-square methods for features selection. Authors used the same labeled dataset. It was collected from Kaggle (Instagram and Twitter). Authors calculated the accuracy and running time for many machines learning classifiers, including SVM, Decision Tree (C4.5), Naïve Bayes Multinomial, and K Nearest Neighbors (KNN). Authors compared the accuracy of classifiers under different conditions, NBM classifier was the most efficient classifier before features selection is implemented, while IBk achieved the most efficient when 500 features were selected. As shown in table two.

A study by  [23] , authors used different methods of text classification to differentiate between hate ,profanity expressions, and other texts. In determining the baseline, authors used standard lexical characteristics and a linear SVM classifier. Authors applied a linear SVM classifier on three groups of features extracted surface n-grams, word skip-grams, and Brown clusters. The best accuracy (78%) achieved when authors used the character 4-gram model.

In [24], authors used a hate speech lexicon to collect tweets that containing hate speech keywords. It’s used then to label a sample of these tweets under three categories (hate, offensive, normal) speech. Authors trained a multiclass classifier to distinguish these different categories. Authors used bigram, unigram, and trigram features as well as features for the number of characters, words, and syllables in each tweet. In addition, authors included binary and count indicators for hashtags, mentions, retweets, and URLs. Author then tested a variety of models; each model was tested by using 5-fold cross-validation. Authors found that the Logistic Regression and Linear SVM model outperformed other models. For the final model, logistic regression with L2 Regularization were used for the final model. The final model then trained by using the whole dataset to predict the label for each tweet. The best performance is highlighted in table 2.

In [15], authors introduced a novel learning method for cyberbullying recognition called Embedding Enhanced Bag-of-Words (EEBOW).EEBoW mixes BoW (Bag of Words) features, latent semantic features and bullying features .Bullying features are derived from word embedding, capturing the semantic details behind words. Authors reported that the EBoW model outperforms other comparable models, including Semantic-enhanced BOW (SEBOW), BoW, LDA(Latent Dirchilet Allocation), LSA(Latent Semantic Analysis) and BOW . The performance of best model (BoW) is highlighted in table two.

Table 2 provides a comparative summary between the different approaches in Latin cybe4rbullying detection as discussed above

5.Deep-Learning in Cyberbullying detection

Aauthors in [25] , proposed a novel algorithm to detect cyberbullying  . The proposed algorithm is based on a convolution neural network (CNN) with semantics features by utilizing word embedding. It is to eliminate the needs for features extraction process. CNN-CB model consists of four layers: embedding, convolutional, max pooling and dense. Authors then applied the algorithm on  dataset consist of about 39,000 English tweets .Authors then compared the accuracy result with the SVM classifier .Authors reported that the CNN-CB algorithm outperforms classical machine learning with accuracy 95 percent as shown in Table 3.

In [26],  it is presented a novel approach to optimize Twitter cyberbullying detection based on deep learning (OCDD).The proposed approach eliminates the features extraction and selection phases . It’s to preserve the semantics of words by replacing the tweet by a set of word vectors. Authors then fed it to a convolutional neural network (CNN) for classification phase along with metaheuristic optimization a algorithm for parameter tuning. This is to find the optimal or near optimal values.

In [27],  it is proposed an aggregate approach for the two deep learning models. The first one is character-level convolutional neural network (CNN). It’s for capturing the low-level syntactic knowledge from the character series. The second is word-level (long-term recurrent convolutional networks) LRCN. It’s to capture semantic high-level information from word sequence, complementing the CNN model. Authors used dataset contains in total 8,815 comments from Kaggle. Authors reported that the hybrid model’s sensitivity and accuracy are 0.5932 and 0.7081, respectively. Also, the aggregated approach is significantly enhanced the performance as well as outperformed other machine learning methods in cyberbullying detection.

In  [28],  it is proposed a novel pronunciation-based neural network (PCNN) for cyberbullying detection .The proposed model is to overcome the misclassification that produced from using misspelled words. Authors phoneme text codes as interface for a coevolution neural network. This technique is to correct spelling errors which did not change the pronunciation. Authors then fed it to CNF in order to detect cyberbullying. Authors compared the performance of models using two datasets, collected from Twitter (1313 tweets) and Formspring.me (13,000 messages). Authors also solved the problem of datasets balance with different techniques in order to compare the result between balanced and imbalanced datasets. Authors compared PCNN performance with previous work and found PCNN outperform the other methods.  Authors reported that PCNN performed better when it is applied on the Twitter dataset than Formspring.me dataset. In addition, PCNN and CNN Random model performed better than CNN with pre-trained.

6. Discussion

This section will present the results according to the language and method in three separate sections , following the same order of study that followed in section four.

6.1. Cyberbullying detection in Arabic language.

For Cyberbullying detection in Arabic language, the findings indicate that, SVM classifier is the most used classifier in the classification of Arabic text [6], [17]. Also, most of studies used  Twitter platform as a source to collect the datasets[16]-[18]. For the surveyed papers, there was no unified dataset, and the maximum size of the dataset is 35273 tweets built by the writers in a study [17],and this size is small compared to the English dataset available. In addition, the results show that most common investigated feature is N-Grams particularly unigram and bigram [6], [18] . The common used method for measuring the accuracy are same to other languages (Precesion,Recall,F1). Furthermore, the highest accuracy achieved when authors used SVM classifier with Language SentiStrength Lexicon feature in [17] followed by NB classifier that is also investigated in the same study.Generally, it was found that deep learning algorithms have not been researched with the Arabic language as much as in English.

6.2. Cyberbullying detection in Latin language

The results of this analysis concluded that out of seven papers analyzed, the SVM classifier was tested five times, followed by the NB classifier that was examined three times. Also, the most of the datasets are collected from Twitter platform , there is still no unified dataset and the maximum size of the dataset is 25k tweets in [24]. In addition, the results show that most common investigated feature is Bigram, Trigram. The commonly used methods for measuring the accuracy are same to other languages (Precession, Recall, F1).Also,  the highest accuracy is archived by using SVM classifier that represented with bigram, trigram,4-gram features in [21].

6.3. Deep-Learning in Cyberbullying detection

After the deep systematic review, the results show that the CNN is the most common used method in the classification of cyberbullying. Also, CNN method had been investigated five times in different studies under different conditions . The results of review show also, that the most of datasets are collected from Twitter platform as well as there is no unified dataset and the maximum size of the dataset is 39,000 tweets, this is small to investigate the deep learning techniques. In addition for deep learning algorithms, the widely used approach for measuring the accuracy is the same for machine learning algorithms (Precession, Recall, F1). In addition, the highest accuracy is archived in [28] study when CNN is used, and outperformed the machine learning algorithms.

To conclude the result for both languages, the best accuracy was achieved when deep learning approaches were used particularly when CNN is applied in [28]. Also, deep learning algorithms were utilized more in the classification of cyberbullying in the English text, while machine learning used more with the Arabic text.  Moreover, SVM is most common classifier in both Arabic and Latin languages and outperformed the other classifiers, it was examined seven times . Furthermore, N-Gram is the most widely used function for both Arabic and Latin languages with classifiers. The Twitter platform also primarily provides the origins of most of the datasets followed by FormSpring.me .

7. Conclusion

In this paper, we conducted an in-depth analysis of 16 studies on automatic cyberbullying detection methods based on text language. we undertook a deep review of evaluation methods, features, dataset size, language, and dataset source of the latest research in this field.We focused only on techniques that adopted neural network and machine learning algorithms. This is to direct future studies on this topic to a more consistent and compatible perspective on recent works, and to provide a practical and effective implementation for future systems. It was found that the best accuracy was achieved when a deep learning approach is used especially when  CNN used. It was found also that, SVM  is the most common classifier in both Arabic and Latin languages, and outperformed the other classifiers.Also, the most widely used feature is N-Gram especially bigram, trigram. In addition, Twitter is the main source for the collected datasets. Furthermore, there is no unified data sets. Although, cyberbullying prevention methods were adopted, largely, but most of the literature work aimed to enhance the detection by adding a new feature, as a number of features increased the process of features selection and extraction become more complicated. On the other hand, most of the work is done to find automated English language solutions, although each language actually has different structures and rules. In addition, there is no standard dataset and list of bad words to be counted as being used in the cyberbullying detection efforts. Finding an effective solution to detect cyberbullying helps a lot in protecting the targeted person.

Conflict of Interest

The authors declare no conflict of interest.

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5. Jenna Snead, Nisa Soltani, Mia Wang, Joe Carson, Bailey Williamson, Kevin Gainey, Stanley McAfee, Qian Zhang, "3D Facial Feature Tracking with Multimodal Depth Fusion", Advances in Science, Technology and Engineering Systems Journal, vol. 10, no. 5, pp. 11–19, 2025. doi: 10.25046/aj100502
6. Glender Brás, Samara Leal, Breno Sousa, Gabriel Paes, Cleberson Junior, João Souza, Rafael Assis, Tamires Marques, Thiago Teles Calazans Silva, "Machine Learning Methods for University Student Performance Prediction in Basic Skills based on Psychometric Profile", Advances in Science, Technology and Engineering Systems Journal, vol. 10, no. 4, pp. 1–13, 2025. doi: 10.25046/aj100401
7. khawla Alhasan, "Predictive Analytics in Marketing: Evaluating its Effectiveness in Driving Customer Engagement", Advances in Science, Technology and Engineering Systems Journal, vol. 10, no. 3, pp. 45–51, 2025. doi: 10.25046/aj100306
8. François Dieudonné Mengue, Verlaine Rostand Nwokam, Alain Soup Tewa Kammogne, René Yamapi, Moskolai Ngossaha Justin, Bowong Tsakou Samuel, Bernard Kamsu Fogue, "Explainable AI and Active Learning for Photovoltaic System Fault Detection: A Bibliometric Study and Future Directions", Advances in Science, Technology and Engineering Systems Journal, vol. 10, no. 3, pp. 29–44, 2025. doi: 10.25046/aj100305
9. Stephen Obare, Kennedy Ogada, "A Review of Natural Language Processing Techniques in Under-Resourced Languages", Advances in Science, Technology and Engineering Systems Journal, vol. 10, no. 2, pp. 35–41, 2025. doi: 10.25046/aj100204
10. Surapol Vorapatratorn, Nontawat Thongsibsong, "AI-Based Photography Assessment System using Convolutional Neural Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 10, no. 2, pp. 28–34, 2025. doi: 10.25046/aj100203
11. Khalifa Sylla, Birahim Babou, Mama Amar, Samuel Ouya, "Impact of Integrating Chatbots into Digital Universities Platforms on the Interactions between the Learner and the Educational Content", Advances in Science, Technology and Engineering Systems Journal, vol. 10, no. 1, pp. 13–19, 2025. doi: 10.25046/aj100103
12. Abhishek Shrestha, Jürgen Großmann, "On Adversarial Robustness of Quantized Neural Networks Against Direct Attacks", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 6, pp. 30–46, 2024. doi: 10.25046/aj090604
13. Win Pa Pa San, Myo Khaing, "Advanced Fall Analysis for Elderly Monitoring Using Feature Fusion and CNN-LSTM: A Multi-Camera Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 6, pp. 12–20, 2024. doi: 10.25046/aj090602
14. Ahmet Emin Ünal, Halit Boyar, Burcu Kuleli Pak, Vehbi Çağrı Güngör, "Utilizing 3D models for the Prediction of Work Man-Hour in Complex Industrial Products using Machine Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 6, pp. 01–11, 2024. doi: 10.25046/aj090601
15. Joshua Carberry, Haiping Xu, "GPT-Enhanced Hierarchical Deep Learning Model for Automated ICD Coding", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 4, pp. 21–34, 2024. doi: 10.25046/aj090404
16. Haruki Murakami, Takuma Miwa, Kosuke Shima, Takanobu Otsuka, "Proposal and Implementation of Seawater Temperature Prediction Model using Transfer Learning Considering Water Depth Differences", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 4, pp. 01–06, 2024. doi: 10.25046/aj090401
17. Brandon Wetzel, Haiping Xu, "Deploying Trusted and Immutable Predictive Models on a Public Blockchain Network", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 3, pp. 72–83, 2024. doi: 10.25046/aj090307
18. Anirudh Mazumder, Kapil Panda, "Leveraging Machine Learning for a Comprehensive Assessment of PFAS Nephrotoxicity", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 3, pp. 62–71, 2024. doi: 10.25046/aj090306
19. Nguyen Viet Hung, Tran Thanh Lam, Tran Thanh Binh, Alan Marshal, Truong Thu Huong, "Efficient Deep Learning-Based Viewport Estimation for 360-Degree Video Streaming", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 3, pp. 49–61, 2024. doi: 10.25046/aj090305
20. Sami Florent Palm, Sianou Ezéckie Houénafa, Zourkalaïni Boubakar, Sebastian Waita, Thomas Nyachoti Nyangonda, Ahmed Chebak, "Solar Photovoltaic Power Output Forecasting using Deep Learning Models: A Case Study of Zagtouli PV Power Plant", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 3, pp. 41–48, 2024. doi: 10.25046/aj090304
21. Taichi Ito, Ken’ichi Minamino, Shintaro Umeki, "Visualization of the Effect of Additional Fertilization on Paddy Rice by Time-Series Analysis of Vegetation Indices using UAV and Minimizing the Number of Monitoring Days for its Workload Reduction", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 3, pp. 29–40, 2024. doi: 10.25046/aj090303
22. Henry Toal, Michelle Wilber, Getu Hailu, Arghya Kusum Das, "Evaluation of Various Deep Learning Models for Short-Term Solar Forecasting in the Arctic using a Distributed Sensor Network", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 3, pp. 12–28, 2024. doi: 10.25046/aj090302
23. Tinofirei Museba, Koenraad Vanhoof, "An Adaptive Heterogeneous Ensemble Learning Model for Credit Card Fraud Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 3, pp. 01–11, 2024. doi: 10.25046/aj090301
24. Marco I. Bonelli, Jiahao Liu, "Revolutionizing Robo-Advisors: Unveiling Global Financial Markets, AI-Driven Innovations, and Technological Landscapes for Enhanced Investment Decisions", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 2, pp. 33–44, 2024. doi: 10.25046/aj090205
25. Toya Acharya, Annamalai Annamalai, Mohamed F Chouikha, "Enhancing the Network Anomaly Detection using CNN-Bidirectional LSTM Hybrid Model and Sampling Strategies for Imbalanced Network Traffic Data", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 1, pp. 67–78, 2024. doi: 10.25046/aj090107
26. Toya Acharya, Annamalai Annamalai, Mohamed F Chouikha, "Optimizing the Performance of Network Anomaly Detection Using Bidirectional Long Short-Term Memory (Bi-LSTM) and Over-sampling for Imbalance Network Traffic Data", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 6, pp. 144–154, 2023. doi: 10.25046/aj080614
27. Renhe Chi, "Comparative Study of J48 Decision Tree and CART Algorithm for Liver Cancer Symptom Analysis Using Data from Carnegie Mellon University", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 6, pp. 57–64, 2023. doi: 10.25046/aj080607
28. Ng Kah Kit, Hafeez Ullah Amin, Kher Hui Ng, Jessica Price, Ahmad Rauf Subhani, "EEG Feature Extraction based on Fast Fourier Transform and Wavelet Analysis for Classification of Mental Stress Levels using Machine Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 6, pp. 46–56, 2023. doi: 10.25046/aj080606
29. Afrodite Papagiannopoulou, Chrissanthi Angeli, "Social Media Text Summarization: A Survey Towards a Transformer-based System Design", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 6, pp. 26–36, 2023. doi: 10.25046/aj080604
30. Nizar Sakli, Chokri Baccouch, Hedia Bellali, Ahmed Zouinkhi, Mustapha Najjari, "IoT System and Deep Learning Model to Predict Cardiovascular Disease Based on ECG Signal", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 6, pp. 08–18, 2023. doi: 10.25046/aj080602
31. Zobeda Hatif Naji Al-azzwi, Alexey N. Nazarov, "MRI Semantic Segmentation based on Optimize V-net with 2D Attention", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 4, pp. 73–80, 2023. doi: 10.25046/aj080409
32. Kohei Okawa, Felix Jimenez, Shuichi Akizuki, Tomohiro Yoshikawa, "Investigating the Impression Effects of a Teacher-Type Robot Equipped a Perplexion Estimation Method on College Students", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 4, pp. 28–35, 2023. doi: 10.25046/aj080404
33. Yu-Jin An, Ha-Young Oh, Hyun-Jong Kim, "Forecasting Bitcoin Prices: An LSTM Deep-Learning Approach Using On-Chain Data", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 3, pp. 186–192, 2023. doi: 10.25046/aj080321
34. Kitipoth Wasayangkool, Kanabadee Srisomboon, Chatree Mahatthanajatuphat, Wilaiporn Lee, "Accuracy Improvement-Based Wireless Sensor Estimation Technique with Machine Learning Algorithms for Volume Estimation on the Sealed Box", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 3, pp. 108–117, 2023. doi: 10.25046/aj080313
35. Chaiyaporn Khemapatapan, Thammanoon Thepsena, "Forecasting the Weather behind Pa Sak Jolasid Dam using Quantum Machine Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 3, pp. 54–62, 2023. doi: 10.25046/aj080307
36. Mario Cuomo, Federica Massimi, Francesco Benedetto, "Detecting CTC Attack in IoMT Communications using Deep Learning Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 2, pp. 130–138, 2023. doi: 10.25046/aj080215
37. Temsamani Khallouk Yassine, Achchab Said, Laouami Lamia, Faridi Mohammed, "Hybrid Discriminant Neural Networks for Performance Job Prediction", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 2, pp. 116–122, 2023. doi: 10.25046/aj080213
38. Der-Jiun Pang, "Hybrid Machine Learning Model Performance in IT Project Cost and Duration Prediction", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 2, pp. 108–115, 2023. doi: 10.25046/aj080212
39. Ivana Marin, Sven Gotovac, Vladan Papić, "Development and Analysis of Models for Detection of Olive Trees", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 2, pp. 87–96, 2023. doi: 10.25046/aj080210
40. Paulo Gustavo Quinan, Issa Traoré, Isaac Woungang, Ujwal Reddy Gondhi, Chenyang Nie, "Hybrid Intrusion Detection Using the AEN Graph Model", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 2, pp. 44–63, 2023. doi: 10.25046/aj080206
41. Víctor Manuel Bátiz Beltrán, Ramón Zatarain Cabada, María Lucía Barrón Estrada, Héctor Manuel Cárdenas López, Hugo Jair Escalante, "A Multiplatform Application for Automatic Recognition of Personality Traits in Learning Environments", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 2, pp. 30–37, 2023. doi: 10.25046/aj080204
42. Ossama Embarak, "Multi-Layered Machine Learning Model For Mining Learners Academic Performance", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 850–861, 2021. doi: 10.25046/aj060194
43. Anh-Thu Mai, Duc-Huy Nguyen, Thanh-Tin Dang, "Transfer and Ensemble Learning in Real-time Accurate Age and Age-group Estimation", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 262–268, 2022. doi: 10.25046/aj070630
44. Fatima-Zahra Elbouni, Aziza EL Ouaazizi, "Birds Images Prediction with Watson Visual Recognition Services from IBM-Cloud and Conventional Neural Network", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 181–188, 2022. doi: 10.25046/aj070619
45. Roy D Gregori Ayon, Md. Sanaullah Rabbi, Umme Habiba, Maoyejatun Hasana, "Bangla Speech Emotion Detection using Machine Learning Ensemble Methods", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 70–76, 2022. doi: 10.25046/aj070608
46. Brahim Zraibi, Mohamed Mansouri, Salah Eddine Loukili, Said Ben Alla, "Hybrid Neural Network Method for Predicting the SOH and RUL of Lithium-Ion Batteries", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 193–198, 2022. doi: 10.25046/aj070520
47. Bahram Ismailov Israfil, "Deep Learning in Monitoring the Behavior of Complex Technical Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 10–16, 2022. doi: 10.25046/aj070502
48. Deeptaanshu Kumar, Ajmal Thanikkal, Prithvi Krishnamurthy, Xinlei Chen, Pei Zhang, "Analysis of Different Supervised Machine Learning Methods for Accelerometer-Based Alcohol Consumption Detection from Physical Activity", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 4, pp. 147–154, 2022. doi: 10.25046/aj070419
49. Nosiri Onyebuchi Chikezie, Umanah Cyril Femi, Okechukwu Olivia Ozioma, Ajayi Emmanuel Oluwatomisin, Akwiwu-Uzoma Chukwuebuka, Njoku Elvis Onyekachi, Gbenga Christopher Kalejaiye, "BER Performance Evaluation Using Deep Learning Algorithm for Joint Source Channel Coding in Wireless Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 4, pp. 127–139, 2022. doi: 10.25046/aj070417
50. Zhumakhan Nazir, Temirlan Zarymkanov, Jurn-Guy Park, "A Machine Learning Model Selection Considering Tradeoffs between Accuracy and Interpretability", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 4, pp. 72–78, 2022. doi: 10.25046/aj070410
51. Ayoub Benchabana, Mohamed-Khireddine Kholladi, Ramla Bensaci, Belal Khaldi, "A Supervised Building Detection Based on Shadow using Segmentation and Texture in High-Resolution Images", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 3, pp. 166–173, 2022. doi: 10.25046/aj070319
52. Tiny du Toit, Hennie Kruger, Lynette Drevin, Nicolaas Maree, "Deep Learning Affective Computing to Elicit Sentiment Towards Information Security Policies", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 3, pp. 152–160, 2022. doi: 10.25046/aj070317
53. Kaito Echizenya, Kazuhiro Kondo, "Indoor Position and Movement Direction Estimation System Using DNN on BLE Beacon RSSI Fingerprints", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 3, pp. 129–138, 2022. doi: 10.25046/aj070315
54. Jayan Kant Duggal, Mohamed El-Sharkawy, "High Performance SqueezeNext: Real time deployment on Bluebox 2.0 by NXP", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 3, pp. 70–81, 2022. doi: 10.25046/aj070308
55. Hanae Naoum, Sidi Mohamed Benslimane, Mounir Boukadoum, "Encompassing Chaos in Brain-inspired Neural Network Models for Substance Identification and Breast Cancer Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 3, pp. 32–43, 2022. doi: 10.25046/aj070304
56. Sreela Sreekumaran Pillai Remadevi Amma, Sumam Mary Idicula, "A Unified Visual Saliency Model for Automatic Image Description Generation for General and Medical Images", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 2, pp. 119–126, 2022. doi: 10.25046/aj070211
57. Idir Boulfrifi, Mohamed Lahraichi, Khalid Housni, "Video Risk Detection and Localization using Bidirectional LSTM Autoencoder and Faster R-CNN", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 6, pp. 145–150, 2021. doi: 10.25046/aj060619
58. Giuseppe Spampinato, Arcangelo Ranieri Bruna, Ivana Guarneri, Davide Giacalone, "Neural Network for 2D Range Scanner Navigation System", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 348–355, 2021. doi: 10.25046/aj060539
59. Seok-Jun Bu, Hae-Jung Kim, "Ensemble Learning of Deep URL Features based on Convolutional Neural Network for Phishing Attack Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 291–296, 2021. doi: 10.25046/aj060532
60. Osaretin Eboya, Julia Binti Juremi, "iDRP Framework: An Intelligent Malware Exploration Framework for Big Data and Internet of Things (IoT) Ecosystem", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 185–202, 2021. doi: 10.25046/aj060521
61. Arwa Alghamdi, Graham Healy, Hoda Abdelhafez, "Machine Learning Algorithms for Real Time Blind Audio Source Separation with Natural Language Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 125–140, 2021. doi: 10.25046/aj060515
62. Baida Ouafae, Louzar Oumaima, Ramdi Mariam, Lyhyaoui Abdelouahid, "Survey on Novelty Detection using Machine Learning Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 73–82, 2021. doi: 10.25046/aj060510
63. Radwan Qasrawi, Stephanny VicunaPolo, Diala Abu Al-Halawa, Sameh Hallaq, Ziad Abdeen, "Predicting School Children Academic Performance Using Machine Learning Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 08–15, 2021. doi: 10.25046/aj060502
64. Fatima-Ezzahra Lagrari, Youssfi Elkettani, "Traditional and Deep Learning Approaches for Sentiment Analysis: A Survey", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 01–07, 2021. doi: 10.25046/aj060501
65. Zhiyuan Chen, Howe Seng Goh, Kai Ling Sin, Kelly Lim, Nicole Ka Hei Chung, Xin Yu Liew, "Automated Agriculture Commodity Price Prediction System with Machine Learning Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 376–384, 2021. doi: 10.25046/aj060442
66. Liang Chen, Mo-How Herman Shen, "A New Topology Optimization Approach by Physics-Informed Deep Learning Process", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 233–240, 2021. doi: 10.25046/aj060427
67. Saichon Sinsomboonthong, "Efficiency Comparison in Prediction of Normalization with Data Mining Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 130–137, 2021. doi: 10.25046/aj060415
68. Hathairat Ketmaneechairat, Maleerat Maliyaem, Chalermpong Intarat, "Kamphaeng Saen Beef Cattle Identification Approach using Muzzle Print Image", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 110–122, 2021. doi: 10.25046/aj060413
69. Valerii Dmitrienko, Serhii Leonov, Aleksandr Zakovorotniy, "New Neural Networks for the Affinity Functions of Binary Images with Binary and Bipolar Components Determining", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 91–99, 2021. doi: 10.25046/aj060411
70. Anjali Banga, Pradeep Kumar Bhatia, "Optimized Component based Selection using LSTM Model by Integrating Hybrid MVO-PSO Soft Computing Technique", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 62–71, 2021. doi: 10.25046/aj060408
71. Kwun-Ping Lai, Jackie Chun-Sing Ho, Wai Lam, "Exploiting Domain-Aware Aspect Similarity for Multi-Source Cross-Domain Sentiment Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 01–12, 2021. doi: 10.25046/aj060401
72. Md Mahmudul Hasan, Nafiul Hasan, Dil Afroz, Ferdaus Anam Jibon, Md. Arman Hossen, Md. Shahrier Parvage, Jakaria Sulaiman Aongkon, "Electroencephalogram Based Medical Biometrics using Machine Learning: Assessment of Different Color Stimuli", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 3, pp. 27–34, 2021. doi: 10.25046/aj060304
73. Svetlana Segarceanu, George Suciu, Inge Gavăt, "Environmental Acoustics Modelling Techniques for Forest Monitoring", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 3, pp. 15–26, 2021. doi: 10.25046/aj060303
74. Nadia Jmour, Slim Masmoudi, Afef Abdelkrim, "A New Video Based Emotions Analysis System (VEMOS): An Efficient Solution Compared to iMotions Affectiva Analysis Software", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 990–1001, 2021. doi: 10.25046/aj0602114
75. Bakhtyar Ahmed Mohammed, Muzhir Shaban Al-Ani, "Follow-up and Diagnose COVID-19 Using Deep Learning Technique", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 971–976, 2021. doi: 10.25046/aj0602111
76. Showkat Ahmad Dar, S Palanivel, "Performance Evaluation of Convolutional Neural Networks (CNNs) And VGG on Real Time Face Recognition System", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 956–964, 2021. doi: 10.25046/aj0602109
77. Dominik Štursa, Daniel Honc, Petr Doležel, "Efficient 2D Detection and Positioning of Complex Objects for Robotic Manipulation Using Fully Convolutional Neural Network", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 915–920, 2021. doi: 10.25046/aj0602104
78. Kenza Aitelkadi, Hicham Outmghoust, Salahddine laarab, Kaltoum Moumayiz, Imane Sebari, "Detection and Counting of Fruit Trees from RGB UAV Images by Convolutional Neural Networks Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 887–893, 2021. doi: 10.25046/aj0602101
79. Binghan Li, Yindong Hua, Mi Lu, "Advanced Multiple Linear Regression Based Dark Channel Prior Applied on Dehazing Image and Generating Synthetic Haze", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 790–800, 2021. doi: 10.25046/aj060291
80. Md Mahmudul Hasan, Nafiul Hasan, Mohammed Saud A Alsubaie, "Development of an EEG Controlled Wheelchair Using Color Stimuli: A Machine Learning Based Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 754–762, 2021. doi: 10.25046/aj060287
81. Abraham Adiputra Wijaya, Inten Yasmina, Amalia Zahra, "Indonesian Music Emotion Recognition Based on Audio with Deep Learning Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 716–721, 2021. doi: 10.25046/aj060283
82. Antoni Wibowo, Inten Yasmina, Antoni Wibowo, "Food Price Prediction Using Time Series Linear Ridge Regression with The Best Damping Factor", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 694–698, 2021. doi: 10.25046/aj060280
83. Javier E. Sánchez-Galán, Fatima Rangel Barranco, Jorge Serrano Reyes, Evelyn I. Quirós-McIntire, José Ulises Jiménez, José R. Fábrega, "Using Supervised Classification Methods for the Analysis of Multi-spectral Signatures of Rice Varieties in Panama", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 552–558, 2021. doi: 10.25046/aj060262
84. Phillip Blunt, Bertram Haskins, "A Model for the Application of Automatic Speech Recognition for Generating Lesson Summaries", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 526–540, 2021. doi: 10.25046/aj060260
85. Shahnaj Parvin, Liton Jude Rozario, Md. Ezharul Islam, "Vehicle Number Plate Detection and Recognition Techniques: A Review", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 423–438, 2021. doi: 10.25046/aj060249
86. Sebastianus Bara Primananda, Sani Muhamad Isa, "Forecasting Gold Price in Rupiah using Multivariate Analysis with LSTM and GRU Neural Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 245–253, 2021. doi: 10.25046/aj060227
87. Md. Ashfaqul Islam, Maisha Hasnin, Nayeem Iftakhar, Md. Mushfiqur Rahman, "Super Resolution Based Underwater Image Enhancement by Illumination Adjustment and Color Correction with Fusion Technique", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 36–42, 2021. doi: 10.25046/aj060205
88. Prasham Shah, Mohamed El-Sharkawy, "A-MnasNet and Image Classification on NXP Bluebox 2.0", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1378–1383, 2021. doi: 10.25046/aj0601157
89. Byeongwoo Kim, Jongkyu Lee, "Fault Diagnosis and Noise Robustness Comparison of Rotating Machinery using CWT and CNN", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1279–1285, 2021. doi: 10.25046/aj0601146
90. Basavaraj Madagouda, R. Sumathi, "Artificial Neural Network Approach using Mobile Agent for Localization in Wireless Sensor Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1137–1144, 2021. doi: 10.25046/aj0601127
91. Arwa A. Al Shamsi, Sherief Abdallah, "Text Mining Techniques for Sentiment Analysis of Arabic Dialects: Literature Review", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1012–1023, 2021. doi: 10.25046/aj0601112
92. Alisson Steffens Henrique, Anita Maria da Rocha Fernandes, Rodrigo Lyra, Valderi Reis Quietinho Leithardt, Sérgio D. Correia, Paul Crocker, Rudimar Luis Scaranto Dazzi, "Classifying Garments from Fashion-MNIST Dataset Through CNNs", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 989–994, 2021. doi: 10.25046/aj0601109
93. Md Mahmudul Hasan, Nafiul Hasan, Mohammed Saud A Alsubaie, Md Mostafizur Rahman Komol, "Diagnosis of Tobacco Addiction using Medical Signal: An EEG-based Time-Frequency Domain Analysis Using Machine Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 842–849, 2021. doi: 10.25046/aj060193
94. Inna Valieva, Iurii Voitenko, Mats Björkman, Johan Åkerberg, Mikael Ekström, "Multiple Machine Learning Algorithms Comparison for Modulation Type Classification Based on Instantaneous Values of the Time Domain Signal and Time Series Statistics Derived from Wavelet Transform", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 658–671, 2021. doi: 10.25046/aj060172
95. Carlos López-Bermeo, Mauricio González-Palacio, Lina Sepúlveda-Cano, Rubén Montoya-Ramírez, César Hidalgo-Montoya, "Comparison of Machine Learning Parametric and Non-Parametric Techniques for Determining Soil Moisture: Case Study at Las Palmas Andean Basin", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 636–650, 2021. doi: 10.25046/aj060170
96. Imane Jebli, Fatima-Zahra Belouadha, Mohammed Issam Kabbaj, Amine Tilioua, "Deep Learning based Models for Solar Energy Prediction", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 349–355, 2021. doi: 10.25046/aj060140
97. Ndiatenda Ndou, Ritesh Ajoodha, Ashwini Jadhav, "A Case Study to Enhance Student Support Initiatives Through Forecasting Student Success in Higher-Education", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 230–241, 2021. doi: 10.25046/aj060126
98. Lonia Masangu, Ashwini Jadhav, Ritesh Ajoodha, "Predicting Student Academic Performance Using Data Mining Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 153–163, 2021. doi: 10.25046/aj060117
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