1. Introduction

Research is considering the dataset of the CBSE model [1] where the dataset presenting software component selection is trained and during testing of the trained network the confusion matrix is produced. According to the confusion matrix the accuracy, F-score, Recall, and precision values are found. On the other hand, data set of each grade would be passed to a hybrid MVO-PSO optimizer to find an optimized rating for each grade to filter the dataset. It could be said that the optimized value for each result is kept to find accuracy, F-score, Recall, and precision values accordingly. Finally, the comparison of accuracy, F-score, recall as well as precision values for non-optimized dataset to optimized Precision, F-score, and Recall for optimized dataset would be performed.

1.1. CBSE

The method of creating various software projects in different categories has been examined in Software engineering. Computer engineering applications have often been used to achieve this goal. However, the dependability of the software system is a difficult job to predict. CBSE may be regarded as a software reliability mechanism that is able to handle the issue. The broad method that supports the creation of various components depending on current software research was evaluated in terms of component-based software engineering. The newest software is not a simple task for beginners, but CBSE [2] allows developers to minimize efforts during the creation of software. Different influence variables are important in the case of CBSE, such as reusability, reliability, component dependence, and interactions between components. These characteristics promote the development of new software and reduce system complexity. There were many software computing methods that tried to predict software dependability. There are several observations. During software development, the selection of component steps has been discovered. Component-dependent design patterns of software are utilized for the recovery and assembly of components.

1.2. Reinforcement Learning

Reinforcement Learning is determined as Machine Learning. It is a branch of AI. Reinforcement Learning has been considered as a category of Machine Learning. It is also considered a branch of AI. Exactly permit a representative of hardware and computer program to mechanically identify perfect attitude in particular circumstances, for maximizing its efficiency. Exactly a type of Machine Learning method which permits representative of hardware and computer program to mechanically identified perfect attitude within a particular circumstance, for maximizing its efficiency. Reinforcement learning is utilized in various graphical games and uses a multi-agent mechanism to control the approach to environment exploration. It is also integrated with the company of abstraction methods which have the various levels to form powerful games dependent on artificial intelligence.

Figure 1: Reinforcement learning

Agent: The performer in the learning system is an agent. All actions are performed by the agent in the environment. The agent gets a reward as per action.

State: The state represents the current status of the environment that plays a significant role in assigning a reward to agents.

Environment: All action of agents is performed in the environment and rewards are providing to the agent as per the state of the environment.

Action: It is a method of representative due to which it communicates and exchanges its setting, in the middle of states. Whenever an action is executed by representatives it gives output inform of reward-dependent on the setting.

Reward: A reward in RL is part of the feedback from the environment. When an agent interacts with the environment, he could observe changes in state and reward signals through his actions.

1.3.   Recurrent Neural Network

RNN network has been considered as a class of neural networks where interconnectivity among nodes is forming a directed graph. It is also creating a temporal sequence which is allowing it to show temporal dynamic behavior. RNN is capable to utilize their memory to compute sequences of inputs of different size as these are inherited from neural networks that are based on feed-forward. It is making them unable to implement operations like un segmented as well as interconnected consideration. RNN has been utilized to take into account two different broad categories of networks that are supporting the usual structure. Here one is having finite impulse. But another one is having impulse which is infinite. Such categories of networks have exhibited runtime actions that are not permanent.

1.3.1. Long Short-Term Memory

LSTM is a well-recognized artificial RNN. This is often used in the field of profound education. Feedback connectivity is provided by LSTM. It is not like a neural network feed. Not only are single data points like graphs processed. Sequences of information such as audio and video are also completed. In the case of LSTM networks, categorization is deemed appropriate. It does process and predicts based on information from time series. This is because there may be temporal delays not known throughout time series in important occurrences.

1.3.2. RNN and LSTM

An ongoing neural network is also known as an RNN. The category of ANN is examined. The node connections provide a network guided by a graph. You accomplished this with the sequence of time. Standard recurring neural networks have disappearances and explosions. LSTM networks were regarded as RNN types. Besides conventional units, LSTM is supported by special units.

1.3.3. Resolving Overfitting Problem by Dropout Layer

The dropout layer is playing a significant role in resolving the issue of over-fitting. The issue of over-fitting arises during the training of the neural network model. The dropout layer is used to handle such issues. If the training is continued, then the model adopts idiosyncrasies. Sometime training becomes less suitable for data that is new to it. This data could be different samples from the population. The model is considered to over-fit when it is too well-adapted to training as well as validating data.

Over-fitting is traced during plotting by checking the validation loss. The model is over-fitting when training loss is constant or it is decreasing. Techniques known as regularizes are used to minimize the influence of over-fitting. Dropout has been considered one out of them.

Dropout is working by eliminating or dropping out the inputs to layer. These could be input variables in a sample of data that is the output of the previous layer. In other words, the Dropout layer is attached to the model among previous layers. It applies to the results of the last layer that have been fed to the next layer. This is influenced by the simulation of a huge network with various network structures. Dropout rate could be considered to layer as chances of configuring every input to layer.

2. Literature Review

There are several types of research in the field of component-based software systems, optimization mechanisms, and neural networks. Researchers have used the SVM as a Classification Method for the Prediction of a defect in software with code metrics. Moreover, research for Performance Modeling of Interaction protocol for CBSD using OOPS based simulation came into existence. After some time, research related to software components selection optimization for CBSE development was made. Researchers applied particle swarm optimization for the performance prediction of the software components. Building models for optimized CBSE was built in several applications development. Some researchers did reliability estimation and performed prediction and measurement of CBSE. A Genetic Algorithm was proposed to manage SVM for predicting components that might be fault-prone. S. Di Martino proposed genetic algorithm. The objective of their research was to set the SVM in order to forecast components that are fault-prone. M. Palviainen did research to estimate reliability. They predicted and measured of component-based software.

In [3], the author applied PSO to software performance prediction.

In [4], the author proposed optimization model. This model has been developed for selection of software component. It has been used in development of several applications.

In [5], the author used SVM based classifier approach for reusability of software components.

In [6], the author performed multi-objective optimization. They did optimization of software architectures. Authors have used Ant Colony Optimization mechanism to accomplish their objective.

In [7], the author did estimation of software reusability in case of component-based system. They made used of several soft computing techniques in their research.

In [8], the author proposed adaptive Neuro fuzzy model. The objective of their research was to predict the reliability in case of component-based software systems.

In [9], the author introduced research on Test case prioritization. This research considered prioritization to perform regression testing. Research made use of ant colony optimization.

In [10], the author proposed research on Neuro-Fuzzy Model in order to find & optimize the Quality as well as Performance in case of CBSE.

In [11], the author presented dynamic mechanism in order to get software components with support of genetic algorithm.

In [12], the author proposed LSTM-based Deep Learning Models. The objective of research was to perform Non-factoid Answer Selection.

In [13], the author did research on multi-Verse Optimizer. They considered it as nature-inspired mechanism in case of global optimization.

In [14], the author proposed research to detect inconsistency in software component. Author made use of ACO and neural network mechanism.

In [15], the author presented deep learning mechanism in case of short-term traffic forecast. The research considered LSTM network.

In [16], the author proposed multiple target deep learning in case of LSTM.

In [17], the author proposed Preference-based component identification by making use of PSO.

In [18], the author proposed research on deep learning in order to perform solar power forecasting. Their approach made use of AutoEncoder along with LSTM Neural Networks.

In [19], the author presented quality assurance by soft computing mechanism. The research focused in field of component-based software.

In [20], the author did quality prediction by making use of ANN. Their research was based on Teaching-Learning Optimization.

In [21], the author proposed multi-objective model for optimization.

In [22], the author did Component selection considering attributes.

In [23], the author did software reliability prediction by making use of Bio Inspired approach.

In [24], the author proposed identification and selection of software component.

In [25], the author proposed model in order to predicting CBS reliability by making used of soft computing mechanism.

In [26], the author proposed enhanced Ant lion Optimizer along with Artificial Neural Network. This research focused on predicting Chinese Influenza.

In [27], the author Imoize considered software reuse and metrics in software engineering.

In [28], the author focused on improvement of reusability of component-based software. Research considered the advantages of software component by making use of data mining.

In [29], the author introduced hybrid Neuro-fuzzy as well as model for feature reduction in order to perform classification.

Table 1: Literature review

3. Problem Statement

Many studies shown the selection of software components, but the optimization mechanism is required for better performance. PSO was used to optimize results in previous studies. However, it is found that MVO offers better performance. In addition, an intelligent model should be introduced that may allow for a deep learning approach using RNN based on LSTM. However, it takes lot of time during training and testing. Then it finds accuracy, f-score according to confusion matrix. The optimization has been included to neural network model for better performance. Through the incorporation of the LSTM MVO-PSO hybrid method, the proposed study is needed to address the performance and accuracy problem.

The proposed work is answer for the accuracy and performance issues faced in previous researches.

4. Proposed Work

In the proposed work, the dataset of the CBSE model is considered. This dataset is trained using a neural network mechanism. During testing of the trained network, the confusion matrix is produced. On the basis of this confusion matrix the accuracy, F-score, Recall, and precision values are calculated. On other hand, the dataset is classified grade-wise in order to get optimized value for each grade. The data set of each grade would be passed to a hybrid MVO-PSO optimizer in order to get the optimized rating for each grade. The data of each grade would be filtered on basis of these optimized values. In another word, the data above the optimized value for each result would be kept. Then the accuracy, F-score, Recall, and precision values are calculated considering this filtered dataset. Then the comparison of accuracy, F-score, Recall, and precision values for non-optimized datasets are made to optimize, F-score, Recall, and precision values for the optimized dataset.

The figure illustrates the proposed system is required to train a component-based selection system that should be capable to predict with maximum accuracy. The trained model for component-based selection would be made with the support of LSTM and simulated in a Matlab environment. Existing researches in component-based selection have provided limited accuracy with limited precision, f-score, and recall value. The implementation of such a model is quite challenging but such research opens doors for innovations. There are several existing types of research that have contributed to the field of component-based selection. It has been observed that previous researches have made use of Fuzzy logic, Genetic algorithm, Machine learning mechanism, KNN classification, LSTM model. But these researches are suffering from accuracy issues.

Figure 2: Architecture of Proposed model

Figure 3: Confusion Matrix

Moreover, the time consumption during training of the network model is more. This research motivates to development of a model that should be trained fast as compared to previous models. Moreover, previous researches have also motivated to increase the accuracy using a two-layer LSTM model considering hidden layer. The proposed research is supposed to provide fast training to the dataset and more accurate prediction as compared to previous researches. The major objective of the research is to study existing literature on various component-based selections. The study and analysis of various component-based selections have been performed during research. Research would propose component-based selection with the support of LSTM layers. Then simulation would be made to perform result analysis. The comparison of existing and proposed work is made afterward.

Long Short-Term Memory networks have been considered as a category of recurrent neural networks. This is found capable to get taught order dependence in case of sequence prediction problems. This is a behavior needed in case of complicated issue domains like translation by machine. Long Short-Term Memory has been considered a complicated field of deep learning. This is difficult to understand Long Short-Term Memory. There has been little work in the field of Long Short-Term Memory. LSTM units are consisting of a ‘memory cell’. These memory cells are capable to maintain data in memory for a large time. Users are moving from RNN to LSTM because it is introducing more controlling knobs. They are capable to manage the flow and mixing of Inputs according to trained Weights. So it provides flexibility during the management of outputs. Thus LSTM is providing the ability to manage and good results.

4.1. Performance Parameters

In this section, we will define the following parameters and confusion matrix is produced using true positive (TP), true negative (TN), false positive (FP), false negative (FN).

TP: True positives have been considered as correctly predicted positive values. In other words, the value of the real category is true and the value of the category that has been predicted is also true.

TN: True negative have been considered as correctly predicted negative values. In other words, the value of the real category is false and the value of the category that has been predicted is also false.

• FP: False positive is the case when the actual category is false but the predicted category is true.
• FN: False-negative is the case when the actual category is true but the predicted category is false.

Parameters utilized to confirm results have been f-score, recall, accuracy and precision which have been explained as follow:

1. Accuracy has been considered as intuitive performance measure. This is the ratio of correctly forecasted findings to total findings.

Accuracy = (True Positive + True Negative) / (True Positive + False Positive + False Negative + True Negative)

2. Precision has been considered as ratio of positive observations that have been correctly predicted to total predicted positive observations.

Precision = True Positive / (True Positive + False Positive)

3. Recall has been considered as ratio of positive observations that have been predicted in correct manner to overall findings in real class – yes.

Recall = (True positive) / (True Positive + False Negative)

4. F1 Score has been weighted average in case of Precision as well as Recall. Score is taking false positives as well as false negatives in consideration.

F1 Score = 2 x (Recall x Precision) / (Recall value + Precision)

5. Simulation

In this section, dataset of 629 packages have been considered for training purposed in research where the average rating is available considering factors such as number of reviews, total sentences, feature requests, feature requests in percentage, problem discoveries, problem discoveries in percentage, GUI Contents, Feature and Functionality, Improvement, Pricing, Resources, Security. A network model has been trained considering this dataset.

The grade is allotted according to the average rating

if Average Rating >4.5 then grade is A

if Average Rating lies between 4 and 4.5 then grade is B

if Average Rating lies between 3 and 4 then grade is C

if Average Rating <3 then grade is D

The classification of record counts according to grade have been discussed below

Table 2: Grade wise record count before optimization

In order to get 100% accuracy, there is need of following confusion matrix

Table 3: Confusion matrix required to get 100% accuracy

But of the model is trained without optimization the confusion matrix is

Table 4: Confusion matrix before optimization

Considering above confusion matrix overall accuracy has been found

Results

TP: 616

Overall Accuracy: 97.93%

Table 5: Accuracy, precision, recall, f1 score in case of non-optimized dataset for 4 classes

5.1. Optimization of GRADE A

Hybrid MVO-PSO is applied in order to get the optimized data set for training;

Hybrid MVO-PSO optimization of GRADE A results in

At iteration 50 the best universes fitness is 0.30119

At iteration 100 the best universes fitness is 0.30119

At iteration 150 the best universes fitness is 0.30119

At iteration 200 the best universes fitness is 0.30119

At iteration 250 the best universes fitness is 0.30119

At iteration 300 the best universes fitness is 0.30119

At iteration 350 the best universes fitness is 0.30119

At iteration 400 the best universes fitness is 0.30119

At iteration 450 the best universes fitness is 0.30118

At iteration 500 the best universes fitness is 0.30118

The best solution for class A obtained by MVO is: 4.6807. The best optimal value for class A of the objective function found by MVO is: 0.30118

Elapsed time is 6.498883 seconds.

Figure 4: Hybrid optimization of Group A

After optimization 58 components have been selected where average rating is more than 4.6807

5.2. Optimization for Grade B

At iteration 50 the best universes fitness is 0.30119

At iteration 100 the best universes fitness is 0.30119

At iteration 150 the best universes fitness is 0.30119

At iteration 200 the best universes fitness is 0.30119

At iteration 250 the best universes fitness is 0.30119

At iteration 300 the best universes fitness is 0.30119

At iteration 350 the best universes fitness is 0.30119

At iteration 400 the best universes fitness is 0.30119

At iteration 450 the best universes fitness is 0.30119

At iteration 500 the best universes fitness is 0.30118

The best solution for class B obtained by Hybrid MVO-PSO is: 4.2356. The best optimal value for class B of the objective function found by Hybrid MVO-PSO is: 0.30118. Elapsed time is 3.650676 seconds.

Figure 5: Hybrid MVO-PSO simulation to get optimized value for grade B

After optimization 120 components have been selected where average rating is more than 4.2356

Figure 6: Hybrid MVO-PSO simulations to get optimized value for grade C

5.3. Optimization of Grade C

At iteration 50 the best universes fitness is 0.3017

At iteration 100 the best universes fitness is 0.3017

At iteration 150 the best universes fitness is 0.3017

At iteration 200 the best universes fitness is 0.3017

At iteration 250 the best universes fitness is 0.3017

At iteration 300 the best universes fitness is 0.30149

At iteration 350 the best universes fitness is 0.3014

At iteration 400 the best universes fitness is 0.30134

At iteration 450 the best universes fitness is 0.30132

At iteration 500 the best universes fitness is 0.30131

The best solution for class A obtained by MVO is: 3.6263. The best optimal value for class A of the objective function found by MVO is: 0.30131. Elapsed time is 3.911880 seconds.

After optimization 139 components have been selected where average rating is more than 3.6263

5.4. Optimization of Grade D

At iteration 50 the best universes fitness is 0.30156

At iteration 100 the best universes fitness is 0.30156

At iteration 150 the best universes fitness is 0.30156

At iteration 200 the best universes fitness is 0.30156

At iteration 250 the best universes fitness is 0.30156

At iteration 300 the best universes fitness is 0.30156

At iteration 350 the best universes fitness is 0.30153

At iteration 400 the best universes fitness is 0.30151

At iteration 450 the best universes fitness is 0.3015

At iteration 500 the best universes fitness is 0.30149

The best solution for class A obtained by MVO is: 2.4891. The best optimal value for class A of the objective function found by MVO is: 0.30149. Elapsed time is 2.961058 seconds.

Figure 7: Hybrid MVO-PSO simulations to get optimized value for grade D

After optimization 31 components have been selected where average rating is more than 2.4891. After grade wise optimization the following components would be selected according to grade.

Table 6: Grade wise record count after optimization

But of the model is trained with optimization the confusion matrix is

Table 7: Confusion matrix produced after filter dataset considering optimization value

Considering above confusion matrix overall accuracy has been found

Results

TP: 345

Overall Accuracy: 99.14%

Table 8: Accuracy, precision, recall, f1 score in case of optimized dataset for 4 classes

6. Comparative Analysis

This section is comparing the accuracy, precision, recall and F1 score before optimization and after optimization. Comparison of accuracy for previous and optimized data set has been shown in table 9.

Comparison of Accuracy for previous and optimized data set has been shown below

Figure 8: Comparison of accuracy

Comparison of precision for previous and optimized data set has been shown below

Figure 9: Comparison of precision

Comparison of recall for previous and optimized data set has been shown below

Figure 10: Comparison of recall

Comparison of fscore for previous and optimized data set has been shown below

Figure 11: Comparison of Fscore

Table 9: Comparison of Accuracy, Precision, Recall, Fscore

7. Conclusion

It has been concluded from simulation that the optimized dataset is capable to produce more accurate result as compared to non-optimized mechanism. Research has considered training and testing of dataset after Optimizing Software Component by deep neural network mechanism to improve the accuracy at the time of software selection. Deep Neural-Network mechanism has performed training and testing to get the confusion metrics of true positive/negative and false positive/negative. Training has been performed using LSTM neural network mechanism to produce confusion matrix for getting accuracy, F-score, Recall and precision values. Simulation result concludes that the accuracy, F-score, Recall and precision values in case of optimized mechanism are better than non-optimized mechanism.

Table 10 shows that proposed work is providing high reliability and feasibility as compare to previous research models.

Table 10: Comparison of proposed work to existing researches

8. Scope of Research

Such research could play a significant role in the field of software development, AI, big data processing, and many other fields where prediction is the major objective. Such a mechanism is suitable to provide an efficient and accurate approach to perform forecasting and decision-making in different areas. Moreover, further researches could use this research as a base in order to get more fruitful results.

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8. 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
9. 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
10. 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
11. 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
12. 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
13. 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
14. 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
15. 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
16. 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
17. 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
18. 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
19. 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
20. 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
21. 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
22. 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
23. 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
24. 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
25. Sathyabama Kaliyapillai, Saruladha Krishnamurthy, Thiagarajan Murugasamy, "An Ensemble of Voting- based Deep Learning Models with Regularization Functions for Sleep Stage Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 1, pp. 84–94, 2023. doi: 10.25046/aj080110
26. 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
27. 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
28. 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
29. 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
30. 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
31. 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
32. 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
33. 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
34. 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
35. 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
36. 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
37. 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
38. 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
39. 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
40. 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
41. 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
42. 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
43. 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
44. 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
45. 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
46. 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
47. 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
48. 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
49. 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
50. 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
51. 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
52. 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
53. 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
54. 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
55. Dejun Chen, Congcong Xiong, Li Guo, Ming Zhong, "A Hybrid NMF-AttLSTM Method for Short-term Traffic Flow Prediction", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 175–184, 2021. doi: 10.25046/aj060220
56. 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
57. 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
58. 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
59. 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
60. 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
61. Reem Bayari, Ameur Bensefia, "Text Mining Techniques for Cyberbullying Detection: State of the Art", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 783–790, 2021. doi: 10.25046/aj060187
62. 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
63. Karamath Ateeq, Manas Ranjan Pradhan, Beenu Mago, "Elasticity Based Med-Cloud Recommendation System for Diabetic Prediction in Cloud Computing Environment", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1618–1633, 2020. doi: 10.25046/aj0506193
64. Revanna Sidamma Kavitha, Uppara Eranna, Mahendra Nanjappa Giriprasad, "A Computational Modelling and Algorithmic Design Approach of Digital Watermarking in Deep Neural Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1560–1568, 2020. doi: 10.25046/aj0506187
65. Xianxian Luo, Songya Xu, Hong Yan, "Application of Deep Belief Network in Forest Type Identification using Hyperspectral Data", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1554–1559, 2020. doi: 10.25046/aj0506186
66. Majdouline Meddad, Chouaib Moujahdi, Mounia Mikram, Mohammed Rziza, "Optimization of Multi-user Face Identification Systems in Big Data Environments", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 762–767, 2020. doi: 10.25046/aj050691
67. Yohei Yamauchi, Mitsuyuki Saito, "Adaptive Identification Method of Vehicle Model for Autonomous Driving Robust to Environmental Disturbances", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 710–717, 2020. doi: 10.25046/aj050685
68. Marcel Nicola, Marian Duță, Maria-Cristina Nițu, Ancuța-Mihaela Aciu, Claudiu-Ionel Nicola, "Improved System Based on ANFIS for Determining the Degree of Polymerization", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 664–675, 2020. doi: 10.25046/aj050680
69. Khalid Chennoufi, Mohammed Ferfra, "Fast and Efficient Maximum Power Point Tracking Controller for Photovoltaic Modules", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 606–612, 2020. doi: 10.25046/aj050674
70. Kin Yun Lum, Yeh Huann Goh, Yi Bin Lee, "American Sign Language Recognition Based on MobileNetV2", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 481–488, 2020. doi: 10.25046/aj050657
71. Miroslav Kratky, Vaclav Minarik, Michal Sustr, Jan Ivan, "Electronic Warfare Methods Combatting UAVs", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 447–454, 2020. doi: 10.25046/aj050653
72. Gede Putra Kusuma, Jonathan, Andreas Pangestu Lim, "Emotion Recognition on FER-2013 Face Images Using Fine-Tuned VGG-16", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 315–322, 2020. doi: 10.25046/aj050638
73. Lubna Abdelkareim Gabralla, "Dense Deep Neural Network Architecture for Keystroke Dynamics Authentication in Mobile Phone", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 307–314, 2020. doi: 10.25046/aj050637
74. Helen Kottarathil Joy, Manjunath Ramachandra Kounte, "A Comprehensive Review of Traditional Video Processing", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 274–279, 2020. doi: 10.25046/aj050633
75. Kailerk Treetipsounthorn, Thanisorn Sriudomporn, Gridsada Phanomchoeng, Christian Dengler, Setha Panngum, Sunhapos Chantranuwathana, Ali Zemouche, "Vehicle Rollover Detection in Tripped and Untripped Rollovers using Recurrent Neural Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 228–238, 2020. doi: 10.25046/aj050627
76. Andrea Generosi, Silvia Ceccacci, Samuele Faggiano, Luca Giraldi, Maura Mengoni, "A Toolkit for the Automatic Analysis of Human Behavior in HCI Applications in the Wild", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 185–192, 2020. doi: 10.25046/aj050622
77. Fei Gao, Jiangjiang Liu, "Effective Segmented Face Recognition (SFR) for IoT", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 36–44, 2020. doi: 10.25046/aj050605
78. Pamela Zontone, Antonio Affanni, Riccardo Bernardini, Leonida Del Linz, Alessandro Piras, Roberto Rinaldo, "Supervised Learning Techniques for Stress Detection in Car Drivers", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 22–29, 2020. doi: 10.25046/aj050603
79. Sherif H. ElGohary, Aya Lithy, Shefaa Khamis, Aya Ali, Aya Alaa el-din, Hager Abd El-Azim, "Interactive Virtual Rehabilitation for Aphasic Arabic-Speaking Patients", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 1225–1232, 2020. doi: 10.25046/aj0505148
80. Daniyar Nurseitov, Kairat Bostanbekov, Maksat Kanatov, Anel Alimova, Abdelrahman Abdallah, Galymzhan Abdimanap, "Classification of Handwritten Names of Cities and Handwritten Text Recognition using Various Deep Learning Models", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 934–943, 2020. doi: 10.25046/aj0505114
81. Zahra Jafari, Saman Rajebi, Siyamak Haghipour, "Using the Neural Network to Diagnose the Severity of Heart Disease in Patients Using General Specifications and ECG Signals Received from the Patients", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 882–892, 2020. doi: 10.25046/aj0505108
82. Khalid A. AlAfandy, Hicham Omara, Mohamed Lazaar, Mohammed Al Achhab, "Using Classic Networks for Classifying Remote Sensing Images: Comparative Study", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 770–780, 2020. doi: 10.25046/aj050594
83. Gökalp Çınarer, Bülent Gürsel Emiroğlu, Recep Sinan Arslan, Ahmet Haşim Yurttakal, "Brain Tumor Classification Using Deep Neural Network", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 765–769, 2020. doi: 10.25046/aj050593
84. Khalid A. AlAfandy, Hicham, Mohamed Lazaar, Mohammed Al Achhab, "Investment of Classic Deep CNNs and SVM for Classifying Remote Sensing Images", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 652–659, 2020. doi: 10.25046/aj050580
85. Lana Abdulrazaq Abdullah, Muzhir Shaban Al-Ani, "CNN-LSTM Based Model for ECG Arrhythmias and Myocardial Infarction Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 601–606, 2020. doi: 10.25046/aj050573
86. Chigozie Enyinna Nwankpa, "Advances in Optimisation Algorithms and Techniques for Deep Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 563–577, 2020. doi: 10.25046/aj050570
87. Andi Aljabar, Suharjito, "BISINDO (Bahasa Isyarat Indonesia) Sign Language Recognition Using CNN and LSTM", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 282–287, 2020. doi: 10.25046/aj050535
88. Sathyabama Kaliyapillai, Saruladha Krishnamurthy, "Differential Evolution based Hyperparameters Tuned Deep Learning Models for Disease Diagnosis and Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 253–261, 2020. doi: 10.25046/aj050531
89. Hicham Moujahid, Bouchaib Cherradi, Oussama El Gannour, Lhoussain Bahatti, Oumaima Terrada, Soufiane Hamida, "Convolutional Neural Network Based Classification of Patients with Pneumonia using X-ray Lung Images", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 167–175, 2020. doi: 10.25046/aj050522
90. Mohsine Elkhayati, Youssfi Elkettani, "Towards Directing Convolutional Neural Networks Using Computational Geometry Algorithms: Application to Handwritten Arabic Character Recognition", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 137–147, 2020. doi: 10.25046/aj050519
91. Nghia Duong-Trung, Luyl-Da Quach, Chi-Ngon Nguyen, "Towards Classification of Shrimp Diseases Using Transferred Convolutional Neural Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 724–732, 2020. doi: 10.25046/aj050486
92. Tran Thanh Dien, Nguyen Thanh-Hai, Nguyen Thai-Nghe, "Deep Learning Approach for Automatic Topic Classification in an Online Submission System", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 700–709, 2020. doi: 10.25046/aj050483
93. Adonis Santos, Patricia Angela Abu, Carlos Oppus, Rosula Reyes, "Real-Time Traffic Sign Detection and Recognition System for Assistive Driving", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 600–611, 2020. doi: 10.25046/aj050471
94. Amar Choudhary, Deependra Pandey, Saurabh Bhardwaj, "Overview of Solar Radiation Estimation Techniques with Development of Solar Radiation Model Using Artificial Neural Network", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 589–593, 2020. doi: 10.25046/aj050469
95. Mohammed Qbadou, Intissar Salhi, Hanaâ El fazazi, Khalifa Mansouri, Michail Manios, Vassilis Kaburlasos, "Human-Robot Multilingual Verbal Communication – The Ontological knowledge and Learning-based Models", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 540–547, 2020. doi: 10.25046/aj050464
96. Marco Bindi, Igor Aizenberg, Riccardo Belardi, Francesco Grasso, Antonio Luchetta, Stefano Manetti, Maria Cristina Piccirilli, "Neural Network-Based Fault Diagnosis of Joints in High Voltage Electrical Lines", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 488–498, 2020. doi: 10.25046/aj050458
97. Deborah Ooi Yee Hui, Syaheerah Lebai Lutfi, Syibrah Naim, Zahid Akhtar, Ahmad Sufril Azlan Mohamed, Kamran Siddique, "The Sound of Trust: Towards Modelling Computational Trust using Voice-only Cues at Zero-Acquaintance", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 469–476, 2020. doi: 10.25046/aj050456
98. Maroua Abdellaoui, Dounia Daghouj, Mohammed Fattah, Younes Balboul, Said Mazer, Moulhime El Bekkali, "Artificial Intelligence Approach for Target Classification: A State of the Art", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 445–456, 2020. doi: 10.25046/aj050453
99. Roberta Avanzato, Francesco Beritelli, "A CNN-based Differential Image Processing Approach for Rainfall Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 438–444, 2020. doi: 10.25046/aj050452
100. Sreela Sreekumaran Pillai Remadevi Amma, Sumam Mary Idicula, "Keyword Driven Image Description Generation System", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 405–411, 2020. doi: 10.25046/aj050447