1. Introduction

Diabetes is a chronic disease that begins with the failure of pancreas. The pancreas fails to produce sufficient insulin required by the body [1]. The internal changes prompt to an increased concentration of glucose in the blood. It is a condition of high blood glucose level in diabetic patients. It can cause either Type I or Type II diabetes. Type I is known as insulin dependent diabetes, which occurs when there is lack of insulin production. Type II diabetes is non-insulin dependent which is caused by the ineffective use of insulin by human body. This will result in excess
body weight and physical inactivity. An earlier prediction or recommendation system is needed to save the patients from the risk of diabetes. In such a condition, Data Mining is suggested and found to be a better diagnostic tool which can be used by the medical practioners too.

Data Mining is the process of selecting, exploring and modeling large amounts of data [2], [3]. This process has become an increasingly pervasive activity in all areas of medical science research. Data mining has resulted in the discovery of useful hidden patterns from massive databases. Consequently, data mining tools are now being used for clinical data. The bottle neck
in data analysis is now raising the most appropriate clinical questions and using proper data and analysis techniques to obtain clinically relevant answers [4]. But as the accumulated data is increasing abundantly, Data Mining algorithm alone is not enough to retrieve hidden pattern from the data.
In order to improve the existing algorithm performance, the computing and storage resources are insufficient in traditional data mining environment. In order to overcome the resource challenges, now day’s resources are utilized on-demand elastic manner with the development of Cloud Computing [5]. National Institute of Standards and Technology (NIST) defines [6] “Cloud Computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (eg., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction”.
The main features of cloud computing are on-demand self-service, broad network access, resource pooling, rapid elasticity and measured service. Cloud computing has three service models such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). It also has four deployment models such as public cloud, private cloud, hybrid cloud and community cloud. Elasticity is used to utilizing the cloud computing resources (Storage, Virtual machines, Servers, Platforms, network) in an elastic way based on the workload requirement. Elasticity manages the ability to increase or decrease the cloud system resources. Elasticity is defined by NIST [7], “Rapid elasticity: Capabilities can be elastically provisioned and released, in some cases automatically, to scale rapidly outward and inward commensurate with demand. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be appropriated in any quantity at any time.”
Elastic resource allocation is completed with the help of proper workload prediction and scheduling which are used to improve the performance of the cloud system. There are no existing algorithms that focus on workload prediction with elasticity in cloud computing environment [8]-[10].
In this research work, an Elastic Medical Cloud Recommendation System is proposed for diabetic classification, where Adaptively Toggle Genetic Algorithm is applied for the Elastic Resource Allocation. HC2A is involved to classify the diabetes patients and in case of diseased patients, they are clustered into three types and the recommendation system is suggested based on the cluster.
The remaining work of the paper is formed as follows. Related works are presented in section 2. In section 3, the proposed algorithm is explained. Results and discussions are presented in section 4. Section 5 shows the conclusion and future directions of the work.

2. Related Works

One of the important feature in cloud computing is elasticity which provides resources in an on-demand elastic manner for cloud users and providers. In [11]the authors concentrated energy consumption in cloud computing resources with the support of elasticity. In [12] the authors designed a CBIHCS (Cloud Based Intelligent Health Care Service) for supervising user health data for diabetic detection and proposed simple heuristic for dynamic resource elasticity.
Resource scaling methods are explained by in [13] based on the queuing models. It contains database level, application level and storage level. Each levels were analyzed with the parameters such as throughput, resource utilization rate and response time. Based on the results to scale applications in cloud environment, they recommended the elastic cloud resource allocation algorithm. In [14] authors proposed an improvised genetic approach for effective cloud resource allocation by maintaining vertical elasticity in cloud IaaS environment. They concentrated on vertical elasticity which focuses virtual machines allocation on cloud servers based on the workloads to improve resource allocation. The Enhanced Genetic algorithm concentrated only on cloud IaaS environment.
In [15], the scaling techniques of elastic resource management were classified as reactive or predictive imminent. Based on the workload requirements the system react without pre-planning in reactive imminent. In predictive imminent, the workload predicts the requirement of cloud servers that are allocated to handle the workload. In [16] it is proposed a cloud based framework with minimum resource setting for monitoring and diagnosis the PD (Parkinson’s Disease). The framework used cloud database, voice data and FBANN (Feedforward Backpropagation based Artificial Neural Network) classifier in cloud platform.
The role played by evolutionary algorithms and Clustering algorithms in Medical Data Mining is incredible. K means algorithm was used in [17] to remove noisy data and Genetic Algorithm was applied to find the optimal set of features. Finally Support Vector Machine (SVM) was used for classifying the diabetic patients. The proposed method has obtained an accuracy of about 96.71%. Fuzzy C Means Algorithm (FCM) [18] was used to determine the small clusters in Diabetic data set and Outlier detection method was applied for classification. The accuracy was 93%. Attribute Weighting method [19] called Fuzzy C Means clustering based attribute weighting (FCMAW) was used for classifying the diabetic data set. This method reduced the variance within the attributes and improves the classification accuracy. It was done by transforming the non-linear separable datasets to linearly separable datasets. FCM was applied to find the center of the attributes and the dataset is weighted according to the ratios of the means of attributes to centers of theirs attributes. Then SVM and KNN classifiers were applied to classify the dataset. The accuracy of the method was 84.38%.

3. Proposed Work

3.1. The system design

In order to get maximum benefit from cloud computing, Cloud providers designed the architecture and deployment model. Various cloud architectures are designed by different cloud service providers. The conceptual diagram of cloud architecture is shown in Figure 1. Cloud service providers provide services to cloud users through internet and datacenter. Cloud servers contains memory utilization vector, processing unit and storage. Virtualization in cloud is to run multiple VMs on a single server and sharing these cloud resources among multiple cloud users. Based on the workloads, the VMs are allocated to process the data as per the command of elastic resource manager.

Figure 1: Conceptual diagram of Cloud Computing Architecture

In this work, the task of the ERM (Elastic Resource Manager) has been concentrated with the goal of elastic resource allocation for improving the resource utilization. ERM is responsible for VMs scheduling, Monitoring VMs and Workloads. Figure 2 explains the proposed framework of EMCRS and Figure 3 explains the flows of the proposed EMCRS for diabetic disease prediction.

3.2. The Elastic Resource Manager

The Elastic Load Manager task is to generate the schedule for the allotment of VM to the available cloud servers with the objective to improve the resource utilization. VM handler is responsible for handling VM queue which contains the user requests information. The scheduling algorithm brings the user request from VM queue and assigns it to the scheduler.

Figure 2: Framework for Elasticity based Med-Cloud Recommendation System

Figure 3: The EMCRS for predicting Diabetes

Figure 4: The flow of Adoptively Toggle Genetic Algorithm

3.3. Pseudocode of ATGA (Adaptively Toggle Genetic Algorithm)

Step 1: Begin
Step 2: Generate initial population with randomly generated chromosomes
Step 3: Calculate the objective Values for each chromosome in the current population
Step 4: Observe the fitness distribution of current population based on the fitness variance
Step 5: If the fitness variance is below 0.4
Step 6: Apply the Stochastic Universal Sampling Selection operator
Step 7: Else
Step 8: Apply the Roulette Wheel Selection operator
Step 9: End If
Step 10: Store the parent chromosomes in mating pool
Step 11: Store the best chromosomes from the current population as elite chromosomes
Step 12: Apply the crossover operator for generating offspring chromosomes
Step 13: Apply the Gaussian mutation operator to adaptively mutate genes
Step 14: Update the current population with the offspring chromosomes
Step 15: If termination condition is not reached
Step 16: Go to Step 3
Step 17: Else
Step 18: Output Best Chromosome
Step 19: End If
Step 20: End
3.3.1. Flow chart Adaptively Toggle Genetic Algorithm
The flow chart of adaptively toggle genetic algorithm is presented in figure 4.

3.3.2. Working Principle of Adaptively Toggle Genetic Algorithm

Even though it is a successful tool, many phenomena can affect the performance of Genetic Algorithm and such an important factor is the parameter settings of Genetic Algorithm. Most of the existing research works related to Genetic Algorithm were carried out with static parameter settings. But, inherently the Genetic Algorithm supports self-learning, parallelism and dynamism. Hence, this research work got the insight to improve the Genetic Algorithm by utilizing the adaptive nature of genetic operators. The core idea behind the development of the proposed Adaptively Toggle Genetic (ATG) algorithm is that to intelligently manipulate the selector operator and to adaptively control the mutation operator in order to enhance the performance of the algorithm. So, the two key operations of the proposed ATG algorithm are as follows.
• Toggle the selection operator
• Adaptively change the mutation probability

3.3.2.1. Toggle the selection operator

As selection operator is one of the crucial operators for the convergence of the Genetic Algorithm, the selector operator must be careful chosen and designed in order to make the best individuals to be survived in the successive generations. Various selection operators are already available, but the before applying the selection operators, the characteristics and suitability of them must be analyzed. In this research work, two selection operators are taken namely, roulette wheel selection and stochastic universal sampling selection operators. Though roulette wheel selection operator is the most commonly used selection operator in Genetic Algorithm, it fails when the fitness values of chromosomes in the population are highly scattered. This is due to the state that the high portion of the roulette wheel is assigned to the fittest chromosome where all the other chromosomes only obtain least sectors of the wheel. Therefore, the roulette wheel may provide biased output and it may lead to the premature convergence state. Hence, the proposed ATG algorithm calculates the fitness variance of the population at first and then it determines which selection operator to use at the current iteration of the algorithm. If the fitness variance is high the stochastic universal sampling selection is used, otherwise the roulette wheel selection is used. In this way, the proposed ATG algorithm intelligently toggles between the two selection operators and helps to conquer the premature convergence problem.

3.3.2.2. Adaptively change the mutation probability

The main task of the mutation operator is to provide exploitation in the search space. Further, it can also help to promote the gene diversity in the gene population. But, when the value of mutation probability is set as a constant, it may disturb the solutions in the global convergence state. Thus, the mutation probability value must be changed according to the convergence state of the algorithm. Hence, the mutation probability value in the proposed ATG algorithm is calculated based on the Gaussian curve as a function of the current iteration. In this way, the proposed ATG algorithm can escape from the local optima by adaptively changing the mutation probability value.
Therefore, the two frequent problems associated with Genetic Algorithm namely the local optimum and premature convergence are eliminated by the proposed ATG algorithm through the dynamic switching between selection operators and adaptive control of mutation operator with additional benefits. With help of ATG algorithm, the scheduler optimally allocates the virtual machines for processing the user requests. The user queries are processed for performing the classification task using the proposed HC2A algorithm.

3.4. Proposed Algorithm for Classification

The HC2A is proposed to classify the diabetic patient data set. The Framework works in three phases as Pre-Processing Phase, Classification Phase and Clustering Phase. Figure 5 explains the flow of the proposed HC2A, In the Pre-Processing Phase, the Genetic-Relative Reduct (G-RR) Algorithm is applied to remove the noisy data and eliminate the irrelevant data. The output at this phase will be the reduced data set. The attributes are classified as Conditional and Decision Attributes. At the initial state the variable R is initialized to null value. And the variable best is assigned to 0. The best value is stored temporarily in another variable tmp and R is stored in T.
The consistency of the data set is checked after removing every attributes. If the decision table is consistent, the attributes is removed and the reduced data set is stored. If the classification accuracy of the Conditional Attributes obtained is greater than the classification accuracy of the Decision attributes (RU(X) (C)>X(D)), then the first Generation of Offspring is constructed. The obtained attributes are selected and then mutation and crossover operations taken place.
Input: Diabetic Data Set
Step 1 Start
Step 2 Load Diabetic dataset
Step 3 Initialize the parameters ( R, ) )
Step 4 Call Genetic – Relative Reduct Algorithm
Step 5 Check for Reduced Data set
Step 6 If the data set is reduced
Go to Step 7
else
Go to step 3
Step 7 Initialize the attributes
Step 8 Apply Modified Particle Swarm Optimization – Neural Network Algorithm
Step 9 Check for maximum number of epoch
Step 10 Calculate error and accuracy
Step 11 If the error is minimized
Go to Step 12
else
Go to step 7
Step 12 Apply Fuzzy C Means Clustering Algorithm
Step 13 Check if the output is clustered and ready to suggest the recommendation system
Step 14 If the clustered output is ready
Go to step 15
Else
Go to step 12
Step 15 Stop
Output: Clustered output for the recommendation system
The obtained Reduct set is stored in T. The Decision Attributes which are best are stored in the variable best . The obtained reduced set is stored in R. The process goes on repeating until the optimal data set is obtained. The obtained output is Reduct Set R. After obtaining the optimal data set, the algorithm terminates.
Step 1: Start
Step 2: R=
Step 3: best = 0
Step 4: do
Step 5: tmp = best
Step 6: T=R
Step 7: for x in (C-R)
Step 8: If RU(X) (C)>X(D)
Step 9: Construct the First Generation
Step 10: Selection
Step 11: Crossover
Step 12: Mutation
Step 13: T=RU{X}
Step 14: best = c(D)
Step 15: R=T
Step 16: Until best == tmp
Step 17: Return R
Step 18: end
The next phase is classification phase. In this phase, Modified Particle Swarm Optimization – Neural Network (MPSO-NN) Algorithm is employed to classify the dataset. This phase optimizes the reduced data set and classifies it.
Step 1: Initialize the population
Step 2: Evaluate the fitness of the attribute
Step 3: For each attribute, find the maximum fitness and compare it with the best found so far
Step 4: Pbesti is equal to the location of maximum fitness function
Step 5: Compare Fitness evaluation with population overall Pbest
Step 6: If particle best is greater than gbest, then reset gbest(i) is equal to the current Pbest’s array index and value
Step 7: Calculate Convergence factor
Step 8: Calculate Inertia weight
Step 9: Update velocity and position and new population is generated
Step 10: Adjust acceleration
Step 11: If the data set is optimized
Go to Step 12
else Go to Step 2.
Step 12: Choose the initial weight
Step 13: If the error is minimum
Go to step 21
else
Go to Step 14
Step 14: Apply the optimized dataset to the network
Step 15: Calculate output for every neuron through hidden layer(s) to output layer
Step 16: Calculate Error value at the output layer
Step 17: Update the weight and bias at the output layer
Step 18: Calculate the Error value at the hidden layer
Step 19: Update the weight and bias at the hidden layer
Step 20: Check if the maximum number of epochs reaches
If yes
Go to Step 21
else
Go to Step 14
Step 21 Evaluate the network performance
Step 22: Classified output
At the preliminary stage, the population is initialized and the fitness of the population is calculated. The maximum fitness of each particles is compared with the best found so far as given in (1).

The Pbesti obtained will be equal to the location of the maximum fitness function. The fitness evaluation is then compared with the population’s overall best as in (2).

The convergence factor is calculated as

where C=C1-C2
C1 is the cognitive learning parameter and C2 is the social collaboration parameter. The C1 and C2 always lie between 0 and 2. Then the inertia value is calculated as given in (4). The inertia value provides the balance between the exploration and exploitation. Generally, the inertia value lies between 0.8 and 1.2. Lower value of inertia component speeds up the convergence of swarm to optima. Higher values encourage the exploration of entire search space.

After the reduced data set is optimized, then the optimized data set is fed as input to Multi-Layer Perceptron Network. Back propagation learning is used to train the network. The output for very neuron is calculated in the hidden unit and output unit. It is calculated as

The sigmoid activation function is applied in every layer. The number of neurons in the hidden layer is fixed by

where Nh is the number of hidden neurons in the hidden layer. Ni and No represent the number of neurons in the input layer and output layer. The error at the output layer is calculated using (9) and the weight and bias are updated using (10).

If the maximum number of epochs is reached and the error is minimized the training process is stopped, and the classified output and results are taken. The final output from this framework will be classified as binary output 0 or 1.
In Clustering Phase, the data with value 0, that is the non-diabetic patients are ignored and the diabetic patients with binary value 1 is again clustered using FCM to cluster the diabetes patients into three types as Type 1, Type 2 and Gestational Diabetes. Fuzzy C-Means (FCM) is a clustering method that allows each data point to belong to multiple clusters with varying degrees of membership. The objective function to minimize the FCM is:

where
 D is the number of data points.
 N is the number of clusters.
 m is fuzzy partition matrix exponent for controlling the degree of fuzzy overlap, with m > 1. Fuzzy overlap refers to how fuzzy the boundaries between clusters are, that is the number of data points that have significant membership in more than one cluster.
 xi is the ith data point.
 cj is the center of the jth cluster.
 μij is the degree of membership of xi in the jth cluster. For a given data point, xi, the sum of the membership values for all clusters is one.

FCM performs the following steps during clustering:
Step 1 Randomly initialize the cluster membership values, μij.
Step 2 Calculate the cluster centers:

Step 4 Calculate the objective function, Jm.
Step 5 Repeat steps 2–4 until Jm improves by less than a specified minimum threshold or until after a specified maximum number of iterations.
Table 1: Confusion matrix for MPSO-ANN – PID

Table2: Confusion matrix for GA-RR+MPSO-ANN- PID

Figure 5: The work flow of HC2A

4. Experimental Setup

The proposed hybrid algorithm is implemented in Matrix Laboratory (MATLAB). The source code developed are run on system with Inter ® i5 with 8 GB RAM. The dataset required for experimentation is taken from the University of California (UCI) Website [20], [21].

5. Results and Discussion

The framework is implemented on Cloudsim and Matlab. The metrics like Sensitivity, Specificity, Precision or Positive Predictive Value (PPV), Negative Predictive Value, False Positive Rate (FPR), False Negative Rate and Accuracy is used to evaluate the performance of the network with different number of neurons. Sensitivity is also called as True Positive Rate which measures the proportions of positivies that are correctly identified. Specificity is defined as the measure of proportions of negatives that are correctly identified. It is also denoted as True Negative Rate.Table 3: Comparison of results obtained using different algorithms for PID

where,
TP- True Positive
FN- False Negative
FP- False Positive
TN- True Negative
Pima Indian Diabetes (PID) Data Set is taken from UCI to evaluate the performance of the proposed algorithm. It consists of a total of 8 attributes and one output class A total of 768 instances are taken for experimentation. Table 1 depicts the results obtained using different algorithms. The proposed algorithm after scheduling using the ATGA has given more accuracy when compared with other algorithms.
The proposed HC2A is compared with Modified Particle Swarm Optimization – Artificial Neural Network (MPSO-ANN) Algorithm [22], Genetic Algorithm-Relative Reduct Algorithm combined with MPSO-ANN GA-RR+MPSO-ANN) Algorithm [23], GA-RR and MPSO-ANN combined with Fuzzy K-Means Clustering (GA-RR+MPSO-ANN+FKM). Table1, 2, 4 & 5 explains the Confusion Matrix obtained for different algorithms.
Below Table 3 shows the results that compares different algorithms for PID. Table 4 below shows the confusion matrix results.

Table 4: Confusion matrix for GA-RR+MPSO-ANN+FKM – PID

The Table 5 below shows the results of Confusion Matrix for HC2A- PID.
Table 5: Confusion Matrix for HC2A- PID

The sensitivity rate for PID is 0.99 with the proposed algorithm. The sensitivity rate is increased since ATGA is applied for scheduling. The prediction accuracy is increased to 98%. The other metrics which are used to measure the performane of the proposed framework also supports the algorithm in classifying the diabetes disease. Figure 6 explains the results obtained by using the different algorithms.

Figure 6: Performance of different algorithms

Table 6: Confusion Matrix for MPSO-ANN – USD

Table 7: Confusion Matrix for GA-RR+ MPSO-ANN – USD

This algorithm is again experimented with US diabetes data set (USD) collected from UCI. This data set consists of 10000 instances, with 54 attributes and one output classes. The promising results obtained supports the proposed framework in classifying the diabetes data set even when the number of data set increases, since the ATGA is applied for scheduling the Cloud Server. The results obtained are tabulated in Table 10. Confusion Matrix for MPSO-ANN, GA-RR+MPSO-ANN, GA-RR+MPSO-ANN+FKM and HC2A is given in tables 6,7,8 and 9. Figure 7 gives the performance of different algorithms on USD. Figure 8 compares the performance of the algorithm on PID and USD based on Accuracy. It was observed and evident that the proposed Framework which comprises the ATGA and GA-RR+MPSO-ANN+FCM is most suitable for diabetes prediction even with increase in the number of data set.
Table 8: Confusion Matrix for GA-RR+ MPSO-ANN+FKM – USD

Table 9: Confusion Matrix for HC2A – USD

Figure 7: Performance comparison of different algorithms – USD Data set

The below table 10 shows the Comparison of results that obtained using different algorithms for USD.

Table 10: Comparison of results obtained using different algorithms for USD

Figure 8: Comparison of performance of different algorithms in PID and USD Data sets based on Accuracy

6. Conclusion

Elasticity-based Med Cloud Recommendation System(EMCRS) is proposed for diagnosing the diabetes disease and providing recommendation for the diabetic patients. This framework is implemented on Cloud and hence the resource sharing is made more comfortable. Adaptively Toggle Genetic Algorithm is applied to allocate the cloud resources in elastic manner. In the proposed algorithm when huge spikes of data occur, the classification process and clustering is handed over to virtual manager to scale the data across virtual machines while maintaining the security features of medical data. The proposed algorithm focuses on workload prediction with elasticity in cloud environment, in case of scaling conditions which is a unique feature of the algorithm. Hybrid Classification and Clustering algorithm is applied to classify and cluster the diabetic patients. The accuracy of about 98% is acquired for both PID and USD datasets. Performance metrics taken for evaluation also explores the effectiveness of EMCRS. Hence, it is evident that the proposed recommendation system is well suited for diabetic prediction. The limitation of the algorithm is the level of security on cloud data. The framework can be enhanced to predict and classify other types of financial and industrial data in future.

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10. Yangjun Chen, Bobin Chen, "On Mining Most Popular Packages", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 4, pp. 60–72, 2024. doi: 10.25046/aj090407
11. 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
12. Wordh Ul Hasan, Kimia Tuz Zaman, Shadi Alian, Tianyi Liang, Vikram Pandey, Jun Kong, Cui Tao, Juan Li, "Bridging Culture and Care: A Mobile App for Diabetes Self-Care Honoring Native American Cultural Practices", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 1, pp. 104–113, 2024. doi: 10.25046/aj090110
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. Fazalur Rehman, Safwan Hashmi, "Enhancing Cloud Security: A Comprehensive Framework for Real-Time Detection, Analysis and Cyber Threat Intelligence Sharing", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 6, pp. 107–119, 2023. doi: 10.25046/aj080612
15. Thepparit Banditwattanawong, Masawee Masdisornchote, "Infrastructure-as-a-Service Ontology for Consumer-Centric Assessment", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 6, pp. 37–45, 2023. doi: 10.25046/aj080605
16. 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
17. 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
18. 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
19. 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
20. 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
21. 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
22. Amine Bouaouda, Karim Afdel, Rachida Abounacer, "Meta-heuristic and Heuristic Algorithms for Forecasting Workload Placement and Energy Consumption in Cloud Data Centers", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 1, pp. 01–11, 2023. doi: 10.25046/aj080101
23. 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
24. 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
25. 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
26. 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
27. Basharat Jamil, Lucía Serrano-Luján, José Manuel Colmenar, "On the Prediction of One-Year Ahead Energy Demand in Turkey using Metaheuristic Algorithms", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 4, pp. 79–91, 2022. doi: 10.25046/aj070411
28. 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
29. 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
30. Alvin Thamrin, Haiping Xu, Rui Ming, "Cloud-Based Hierarchical Consortium Blockchain Networks for Timely Publication and Efficient Retrieval of Electronic Health Records", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 2, pp. 179–190, 2022. doi: 10.25046/aj070218
31. 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
32. 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
33. 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
34. 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
35. 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
36. 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
37. Olena Nosovets, Vitalii Babenko, Ilya Davydovych, Olena Petrunina, Olga Averianova, Le Dai Zyonh, "Personalized Clinical Treatment Selection Using Genetic Algorithm and Analytic Hierarchy Process", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 406–413, 2021. doi: 10.25046/aj060446
38. 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
39. 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
40. 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
41. Banir Rimbawansyah Hasanuddin, Sani Muhammad Isa, "Business Intelligence Budget Implementation in Ministry of Finance (As Chief Operating Officer)", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 123–129, 2021. doi: 10.25046/aj060414
42. 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
43. 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
44. 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
45. Carla Blank, Matthew McBurney, Maria Morgan, Raed Seetan, "A Survey of Big Data Techniques for Extracting Information from Social Media Data", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 3, pp. 189–204, 2021. doi: 10.25046/aj060322
46. Juhi Singh, Shalini Agarwal, "Plummeting Makespan by Proficient Workflow Scheduling in Cloud Environment", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 3, pp. 40–44, 2021. doi: 10.25046/aj060306
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. Marlene Ofelia Sanchez-Escobar, Julieta Noguez, Jose Martin Molina-Espinosa, Rafael Lozano-Espinosa, "Supporting the Management of Predictive Analytics Projects in a Decision-Making Center using Process Mining", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 1084–1090, 2021. doi: 10.25046/aj0602123
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. Ahmad AA Alkhatib, Abeer Alsabbagh, Randa Maraqa, Shadi Alzubi, "Load Balancing Techniques in Cloud Computing: Extensive Review", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 860–870, 2021. doi: 10.25046/aj060299
53. Binghan Li, Yindong Hua, Yifeng Liu, Mi Lu, "Dilated Fully Convolutional Neural Network for Depth Estimation from a Single Image", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 801–807, 2021. doi: 10.25046/aj060292
54. 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
55. 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
56. 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
57. 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
58. Rubin Thottupurathu Jose, Sojan Lal Poulose, "Ontology Based Privacy Preservation over Encrypted Data using Attribute-Based Encryption Technique", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 378–386, 2021. doi: 10.25046/aj060244
59. Amany Khalil, Osama Tolba, Sherif Ezzeldin, "Design Optimization of Open Office Building Form for Thermal Energy Performance using Genetic Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 254–261, 2021. doi: 10.25046/aj060228
60. 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
61. 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
62. 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
63. Abba Suganda Girsang, Antoni Wibowo, Jason, Roslynlia, "Comparison between Collaborative Filtering and Neural Collaborative Filtering in Music Recommendation System", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1215–1221, 2021. doi: 10.25046/aj0601138
64. 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
65. 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
66. Subash Pokharel, Aleksandar Dimitrovski, "Ferromagnetic Core Reactor Modeling and Design Optimization", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 810–818, 2021. doi: 10.25046/aj060190
67. 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
68. Sonia Souabi, Asmaâ Retbi, Mohammed Khalidi Idrissi, Samir Bennani, "A Recommendation Approach in Social Learning Based on K-Means Clustering", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 719–725, 2021. doi: 10.25046/aj060178
69. Jesus Aguila-Leon, Cristian Chiñas-Palacios, Carlos Vargas-Salgado, Elias Hurtado-Perez, Edith Xio Mara Garcia, "Particle Swarm Optimization, Genetic Algorithm and Grey Wolf Optimizer Algorithms Performance Comparative for a DC-DC Boost Converter PID Controller", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 619–625, 2021. doi: 10.25046/aj060167
70. 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
71. Abdelouahad Bellat, Khalifa Mansouri, Abdelhadi Raihani, Khalili Tajeddine, "Optimizing the Wind Farm Layout for Minimizing the Wake Losses", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 309–315, 2021. doi: 10.25046/aj060135
72. 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
73. Deisy Chipana-Collahua, Mariluz Chipana-Collahua, Rosa Villegas-Ortiz, Brian Meneses-Claudio, Hernan Matta-Solis, "Type 2 Diabetes Risk and Physical Activity in outpatients treated in Health Centers in a District of North Lima, 2020", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1651–1656, 2020. doi: 10.25046/aj0506196
74. 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
75. Bilal Babayigit, Eda Nur Hascokadar, "A Software-Defined Network Approach for The Best Hospital Localization Against Coronavirus (COVID-19)", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1537–1544, 2020. doi: 10.25046/aj0506184
76. Poonam Ghuli, Manoj Kartik R, Mohammed Amaan, Mridul Mohta, N Kruthik Bhushan, Poonam Ghuli, Shobha G, "Recommendation System for SmartMart-A Virtual Supermarket", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1408–1413, 2020. doi: 10.25046/aj0506170
77. Jared Zavala-Izaguirre, Fanny Mego-Llanos, Sarita Cornejo-Quispitongo, Brian Meneses-Claudio, Hernan Solis-Matta, Lourdes Matta-Zamudio, "Quality of Life in Patients with Type 2 Diabetes of the Central Hospital of the Peruvian Air Force, 2019", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1340–1344, 2020. doi: 10.25046/aj0506160
78. Chaymae Ziani, Abdelalim Sadiq, "SH-CNN: Shearlet Convolutional Neural Network for Gender Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1328–1334, 2020. doi: 10.25046/aj0506158
79. Marouane EL Midaoui, Mohammed Qbadou, Khalifa Mansouri, "A Novel Approach of Smart Logistics for the Health-Care Sector Using Genetic Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1143–1152, 2020. doi: 10.25046/aj0506138
80. Mohamed Hedi Fourati, Soumaya Marzouk, Mohamed Jmaiel, Tom Guerout, "Docker-C2A : Cost-Aware Autoscaler of Docker Containers for Microservices-based Applications", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 972–980, 2020. doi: 10.25046/aj0506116
81. 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
82. 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
83. 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
84. 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
85. 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
86. 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
87. 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
88. 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
89. Puttakul Sakul-Ung, Amornvit Vatcharaphrueksadee, Pitiporn Ruchanawet, Kanin Kearpimy, Hathairat Ketmaneechairat, Maleerat Maliyaem, "Overmind: A Collaborative Decentralized Machine Learning Framework", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 280–289, 2020. doi: 10.25046/aj050634
90. Moulay Youssef Smaili, Hanaa Hachimi, "Hybridization of Improved Binary Bat Algorithm for Optimizing Targeted Offers Problem in Direct Marketing Campaigns", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 239–246, 2020. doi: 10.25046/aj050628
91. 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
92. 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
93. Khalid Ait Hadi, Rafik Lasri, Abdellatif El Abderrahmani, "Inferring Topics within Social Networking Big Data, Towards an Alternative for Socio-Political Measurement", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 155–159, 2020. doi: 10.25046/aj050618
94. 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
95. 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
96. Lincan Li, Chiew Foong Kwong, Qianyu Liu, "A Proactive Mobile Edge Cache Policy Based on the Prediction by Partial Matching", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 1154–1161, 2020. doi: 10.25046/aj0505140
97. Wongpanya Nuankaew, Pratya Nuankaew, "Tolerance of Characteristics and Attributes in Developing Student’s Academic Achievements", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 1126–1136, 2020. doi: 10.25046/aj0505137
98. 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
99. 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
100. 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