bluuebunny/crossref_metadata_2025 huggingface dataset:
Auxetic material in biomedical applications: a systematic review
Authors: Diaz Melgarejo Andrés , Ramírez Jose Luis , Rubiano Astrid
Published: September 2022

This study reviews and analyzes the different auxetic materials that have been developed in recent years. The search for research articles was carried out through one of the largest databases such as ScienceDirect, where 845 articles were collected, of which several filters were carried out to have a base of 386 articles. There are a variety of materials depending on their structure, composition, and industrial application, highlighting biomedical applications from tissue engineering, cell proliferation, skeletal muscle regeneration, transportation, bio-prosthesis to biomaterial. The present paper provides an overview of auxetic materials and its applications, providing a guide for designers and manufacturers of devices and accessories in any industry.
Conducted emission investigation of infant incubator heating control mode
Authors: Khotimah Khusnul , Yoppy Yoppy , Sudrajat Muhammad Imam , Permatasari Vera , Trivida Elvina , Wijarnoko Tyas Ari Wahyu
Published: September 2022

This paper investigates the effect of two different heating power control systems of infant incubators on their conducted emissions. Two infant incubators which respectively employ zero-crossing control mode and phase angle control mode are observed. The research was conducted by measuring conducted electromagnetic interference (EMI) from each infant incubator's power input. Measurements are conducted both during full power condition, while the incubator's compartment temperature is far away from the temperature setpoint, and during power chopping condition, while the compartment temperature reaches the steady-state set point. Method and limit of the measurement refer to CISPR 11. It is found that conducted emission higher than the standard CISPR 11 limit occurs during power chopping on phase angle control mode. This results from the sharp rise time of voltage delivered to the heater, around 220 ns for each chopping cycle.
A self-balancing platform on a mobile car
Authors: Tawfeeq Bushra Amer , Salloom Maher Yahya , Alkamachi Ahmed
Published: September 2022

In the last years, the self-balancing platform has become one of the most common candidates to use in many applications such as flight, biomedical fields, and industry. In this paper, the physical prototype of a proposed self-balancing platform that described the self-balancing attitude in the (X-axis, Y-axis, or biaxial) under the influence of road disturbance has been introduced. In the physical prototype, the inertial measurement unit (IMU) sensor will sense the disturbance in (X-axis, Y-axis, and biaxial). With the determined error, the corresponding electronic circuit, DC servo motors, and the Arduino software, the platform overcame the tilt angle(disturbance). Optimization of the proportional-integral-derivative (PID) controllers’ coefficients by the genetic algorithm method effectively affected the performance of the platform, as the platform system is stable and the platform was able to compensate for the tilt angle in (X-axis, Y-axis, and both axes) and overcome the error in a time that does not exceed four seconds. Therefore, a proposed self-balancing platform’s physical prototype has a high balancing accuracy and meets operational requirements despite the platform’s simple design.
Controlling the half-step mode operation of the variable reluctance stepper motor by using Mamdani type of fuzzy logic controller
Authors: Mhawesh Mustafa A. , Kashkool Ahmed S.
Published: September 2022

This paper proposed the step angle controlling of the half-step mode operation of the variable reluctance stepper motor (VRSM) by using Mamdani type of fuzzy logic controller (FLC). The MATLAB program was used to achieve the approach. The VRSM that was used in this paper has six stator poles and four rotor poles. The VRSM has three phases that represent the input variables and the step angle represents the output variable in the FLC in MATLAB. Membership functions were created for the input and output variables. The rules of the FLC were built in MATLAB. The theoretical step angles results of the VRSM were obtained by using mathematically equation while the practical results were obtained by using MATLAB. The obtained results are closer to the actual results depending on the comparison between the theoretical and practical readings. These results were written in table and were plotted in figure.
Control of robot-assisted gait trainer using hybrid proportional integral derivative and iterative learning control
Authors: Parikesit Elang , Maneetham Dechrit , Sutyasadi Petrus
Published: September 2022

An inexpensive exoskeleton of the lower limb was designed and developed in this study. It can be used as a gait trainer for persons with lower limb problems. It plays an essential role in lower limb rehabilitation and aid for patients, and it can help them improve their physical condition. This paper proposes a hybrid controller for regulating the lower limb exoskeleton of a robot-assisted gait trainer that uses a proportional integral and derivative (PID) controller combined with an iterative learning controller (ILC). The direct current motors at the hip and knee joints are controlled by a microcontroller that uses a preset pattern for the trajectories. It can learn how to monitor a trajectory. If the trajectory or load is changed, it will be able to follow the change. The experiment showed that the PID controller had the smallest overshoot, and settling time, and was responsible for system stability. Even if there are occasional interruptions, the tracking performance improves with the ILC.
Optimal trajectory tracking control for a wheeled mobile robot using backstepping technique
Authors: Fadlo Said , Elmahjoub Abdelhafid Ait , Rabbah Nabila
Published: September 2022

This work studies an optimal trajectory tracking of a wheeled mobile robot with the objective of minimizing energy consumption. First, the mathematical model, which takes into account the kinematic model of the mobile robot and the dynamic model of the actuators is presented. Then, a backstepping controller is designed and its parameters are tuned to satisfy several strict criteria such as rapid convergence, matching desired trajectory, and minimizing energy. For that, two cost functions were investigated and the best one has been selected. The significant reduction in energy losses achieved for all the proposed motion scenarios proves the effectiveness of our approach.
Estimation of water momentum and propeller velocity in bow thruster model of autonomous surface vehicle using modified Kalman filter
Authors: Nurhadi Hendro , Kiki Mayga , Adzkiya Dieky , Herlambang Teguh
Published: September 2022

Autonomous surface vehicle (ASV) is a vehicle in the form of an unmanned on-water surface vessel that can move automatically. As such, an automatic control system is essentially required. The bow thruster system functions as a propulsion control device in its operations. In this research, the water momentum and propeller velocity were estimated based on the dynamic bow thruster model. The estimation methods used is the Kalman filter (KF) and ensemble Kalman filter (EnKF). There are two scenarios: tunnel thruster condition and open-bladed thruster condition. The estimation results in the tunnel thruster condition showed that the root mean square error (RMSE) by the EnKF method was relatively smaller, that is, 0.7920 and 0.1352, while the estimation results in the open-bladed thruster condition showed that the RMSE by the KF method was relatively smaller, that is, 1.9957 and 2.0609.
Comparison of window shapes and lengths in short-time feature extraction for classification of heart sound signals
Authors: Fakhry Mahmoud , Brery Abeer FathAllah
Published: September 2022

Heart sound signals, phonocardiography (PCG) signals, allow for the automatic diagnosis of potential cardiovascular pathology. Such classification task can be tackled using the bidirectional long short-term memory (biLSTM) network, trained on features extracted from labeled PCG signals. Regarding the non-stationarity of PCG signals, it is recommended to extract the features from multiple short-length segments of the signals using a sliding window of certain shape and length. However, some window contains unfavorable spectral side lobes, which distort the features. Accordingly, it is preferable to adapt the window shape and length in terms of classification performance. We propose an experimental evaluation for three window shapes, each with three window lengths. The biLSTM network is trained and tested on statistical features extracted, and the performance is reported in terms of the window shapes and lengths. Results show that the best performance is obtained when the Gaussian window is used for splitting the signals, and the triangular window competes with the Gaussian window for a length of 75 ms. Although the rectangular window is a commonly offered option, it is the worst choice for splitting the signals. Moreover, the classification performance obtained with a 75 ms Gaussian window outperforms that of a baseline method.
Computed tomography image analysis for Indonesian total hip arthroplasty designs
Authors: Asmaria Talitha , Mayasari Dita Ayu , Gestara Febrananda Alfensa Dinda , Nurul Nadiya , Rahyussalim Ahmad Jabir , Kartika Ika
Published: September 2022

Total hip arthroplasty purposes to replace a hip joint damaged by an artificial hip joint. However, the developed products that already exist in the market lead to the mismatch between the hip implant equipment and the patient’s bone morphometric. Besides causing complications, the mismatch also continues to the dislocation effects, fracture, osteolysis, and thigh pain. This paper aims to design a customized hip implant based on real patient data, particularly for Indonesian patient, limited to the acetabular components and stem parts. The computed images were analyzed to estimate the patient proximal femur morphometric; those are the femoral head diameter, neck-shaft angle, mediolateral width, anteroposterior width, neck length and neck width. The experiment has succeeded in designing the acetabular shell with the thickness of 3 mm, the acetabular liner with the thickness of 6 mm, the femoral head between 22.4 to 24.8 mm, the short stem in both the right for 110.656 mm and left femur bone for 111.49 mm; that fit the patient's femur bone. Overall, the proposed steps in designing the customized hip implant in this work, based on image analysis on medical imaging data, can be a standard to be applied for other patient-needs hip arthroplasty implants.
Indonesian pharmacy retailer segmentation using recency frequency monetary-location model and ant K-means algorithm
Authors: Palupi Ghea Sekar , Fakhruzzaman Muhammad Noor
Published: September 2022

We proposed an approach of retailer segmentation using a hybrid swarm intelligence algorithm and recency frequency monetary (RFM)-location model to develop a tailored marketing strategy for a pharmacy industry distribution company. We used sales data and plug it into MATLAB to implement ant clustering algorithm and K-means, then the results were analyzed using RFM-location model to calculate each clusters’ customer lifetime value (CLV). The algorithm generated 13 clusters of retailers based on provided data with a total of 1,138 retailers. Then, using RFM-location, some clusters were combined due to identical characteristics, the final clusters amounted to 8 clusters with unique characteristics. The findings can inform the decision-making process of the company, especially in prioritizing retailer segments and developing a tailored marketing strategy. We used a hybrid algorithm by leveraging the advantage of swarm intelligence and the power of K-means to cluster the retailers, then we further added value to the generated clusters by analyzing it using RFM-location model and CLV. However, location as a variable may not be relevant in smaller countries or developed countries, because the shipping cost may not be a problem.  
A survey of ranging techniques for vehicle localization in intelligence transportation system: challenges and opportunities
Authors: Bakhuraisa Yaser , Aziz Azlan Abd , Geok Tan Kim , Jamian Saifulnizan , Mustakim Fajaruddin
Published: September 2022

Observing the vehicles movement becomes an urgent necessity due to exponentially increasing numbers of vehicles in the world. However, to this regard, a good deal of research had been presented to estimate the exact physical position of the vehicle. The major challenges faced vehicle localization systems are large coverage areas required, positioning at diverse environments and positioning during a high-speed movement. However, in this paper, the challenges of employing the vehicle localization techniques, which rely on the propagation signal properties, are discussed. Moreover, a comparison between these techniques, in terms of accuracy, responsiveness, scalability, cost, and complexity, is conducted. The presented positioning technologies are classified into five categories: satellite based, radio frequency based, radio waves based, optical based, and sound based. The discussion shows that, both of satellite-based technology and cellular-based technology are emerge solutions to overcome the challenges of vehicle positioning. Satellite-based can provide a high accurate positioning in open outdoor environment, whereas the cellular-based can provide accurate and reliable vehicle localization in urban environment, it can support non-line of sight (NLOS) positioning and provide large coverage and high data transmission. The paper also shows that, the standalone localization technology still has limitations. Therefore, we discussed how the presented techniques are integrated to improve the positioning performance.
Low-complex Bayesian estimator for imperfect channels in massive muti-input multi-output system
Authors: Shatti Ahmed Hussein , Hussein Ehab AbdulRazzaq
Published: September 2022

Motivated by the fact that the complexity of the computations is one of the main challenges in large multiple input multiple output systems, known as massive multiple-input multiple-output (MIMO) systems, this article proposes a low-complex minimum mean squared error (MMSE) Bayesian channel estimator for uplink channels of such systems. First, we have discussed the necessity of the covariance information for the MMSE estimator and how their imperfection knowledge can affect its accuracy. Then, two reduction phases in dimension and floating-point operations have been suggested to reduce its complexity: in phase 1, eigenstructure reduction for channel covariance matrices is implemented based on some truncation rules, while in phase 2, arithmetic operations reduction for matrix multiplications in the MMSE equation is followed. The proposed procedure has significantly reduced the complexity of the MMSE estimator to the first order O(M), which is less than that required for the conventional MMSE with O(M3) in terms of matrix dimension. It has been shown that the estimated channels using our proposed procedure are asymptotically aligned and serve the same quality as the full-rank estimated channels. Our results are validated by averaging the normalized mean squared error (NMSE) over a length of 500 sample realizations through a Monte Carlo simulation using MATLAB R2020a.
Knowledge graph-based method for solutions detection and evaluation in an online problem-solving community
Authors: Sekkal Houda , Amrous Naïla , Bennani Samir
Published: September 2022

Online communities are a real medium for human experiences sharing. They contain rich knowledge of lived situations and experiences that can be used to support decision-making process and problem-solving. This work presents an approach for extracting, representing, and evaluating components of problem-solving knowledge shared in online communities. Few studies have tackled the issue of knowledge extraction and its usefulness evaluation in online communities. In this study, we propose a new approach to detect and evaluate best solutions to problems discussed by members of online communities. Our approach is based on knowledge graph technology and graphs theory enabling the representation of knowledge shared by the community and facilitating its reuse. Our process of problem-solving knowledge extraction in online communities (PSKEOC) consists of three phases: problems and solutions detection and classification, knowledge graph constitution and finally best solutions evaluation. The experimental results are compared to the World Health Organization (WHO) model chapter about Infant and young child feeding and show that our approach succeed to extract and reveal important problem-solving knowledge contained in online community’s conversations. Our proposed approach leads to the construction of an experiential knowledge graph as a representation of the constructed knowledge base in the community studied in this paper.
Breast cancer diagnosis: a survey of pre-processing, segmentation, feature extraction and classification
Authors: Sadeghi Pour Ehsan , Esmaeili Mahdi , Romoozi Morteza
Published: September 2022

Machine learning methods have been an interesting method in the field of medical for many years, and they have achieved successful results in various fields of medical science. This paper examines the effects of using machine learning algorithms in the diagnosis and classification of breast cancer from mammography imaging data. Cancer diagnosis is the identification of images as cancer or non-cancer, and this involves image preprocessing, feature extraction, classification, and performance analysis. This article studied 93 different references mentioned in the previous years in the field of processing and tries to find an effective way to diagnose and classify breast cancer. Based on the results of this research, it can be concluded that most of today’s successful methods focus on the use of deep learning methods. Finding a new method requires an overview of existing methods in the field of deep learning methods in order to make a comparison and case study.
Novel reliable and dynamic energy-aware routing protocol for large scale wireless sensor networks
Authors: Qabouche Hicham , Sahel Aicha , Badri Abdelmajid , El Mourabit Ilham
Published: September 2022

Wireless sensor networks (WSN) are made up of an important number of sensors, called nodes, distributed in random way in a concerned monitoring area. All sensor nodes in the network are mounted with limited energy sources, which makes energy harvesting on top of the list of issues in WSN. A poor communication architecture can result in excessive consumption, reducing the network lifetime and throughput. Centralizing data collection and the introduction of gateways (GTs), to help cluster heads (CHs), improved WSN life time significantly. However, in vast regions, misplacement and poor distribution of GTs wastes a huge amount of energy and decreases network’s performances. In this work, we describe a reliable and dynamic with energy-awareness routing (RDEAR) protocol that provides a new GT’s election approach taking into consideration CHs density, transmission distance and energy. Applied on 20 different networks, RDEAR reduced the overall energy consumption, increased stability zone and network life time as well as other compared metrics. Our proposed approach increased network’s throughput up to 75.92% , 67.7% and 9.78% compared to the low energy adaptive clustering hierarchy (LEACH), distributed energy efficient clustering (DEEC) and static multihop routing (SMR), protocols, respectively.
Avatar design types and user engagement in digital educational games during evaluation phase
Authors: Mohd Nizam Dinna N. , Nursakinaz Rudiyansah Dylia , Mohd Tuah Nooralisa , Abdullah Sani Zaidatol Haslinda , Sungkaew Kornchulee
Published: September 2022

Avatar design types can range from human representations to abstract representations. In digital educational games (DEGs), avatars are frequently used to encourage users to play the game. However, the role of avatar design types and their engagement in digital games are still unclear and empirically under research. Therefore, a bespoke digital educational game in geography was developed and validated by six expert users. Then forty-five users participated in the evaluation phase to investigate engagement and avatar types on digital educational games using the user engagement scale (UES). The results reported aesthetics and satisfaction factors somehow influenced the avatar design types, but none of the UES subscales was influenced by preferred avatar design types. Moreover, the human-cartoon avatar, which was not entirely human and cartoonish, was the most popular avatar design type among young adults. Other issues discussed for future developers and research included incorporating more avatar design selections into the study, integrating social interaction features into the game, using the same drawing style for avatars and provide easy access to the bespoke game during data collection.
A web content mining application for detecting relevant pages using Jaccard similarity
Authors: Jalal Ahmed Adeeb , Jasim Abdulrahman Ahmed , Mahawish Amar A.
Published: September 2022

The tremendous growth in the availability of enormous text data from a variety of sources creates a slew of concerns and obstacles to discovering meaningful information. This advancement of technology in the digital realm has resulted in the dispersion of texts over millions of web sites. Unstructured texts are densely packed with textual information. The discovery of valuable and intriguing relationships in unstructured texts demands more computer processing. So, text mining has developed into an attractive area of study for obtaining organized and useful data. One of the purposes of this research is to discuss text pre-processing of automobile marketing domains in order to create a structured database. Regular expressions were used to extract data from unstructured vehicle advertisements, resulting in a well-organized database. We manually develop unique rule-based ways of extracting structured data from unstructured web pages. As a result of the information retrieved from these advertisements, a systematic search for certain noteworthy qualities is performed. There are numerous approaches for query recommendation, and it is vital to understand which one should be employed. Additionally, this research attempts to determine the optimal value similarity for query suggestions based on user-supplied parameters by comparing MySQL pattern matching and Jaccard similarity.
Multi-level encryption for 3D mesh model based on 3D Lorenz chaotic map and random number generator
Authors: Ali Nashwan Alsalam , S. Rahma Abdul Monem , H. Shaker Shaimaa
Published: September 2022

The increasing 3D model applications in various areas of life and widespread use like industry leads to 3D models being stolen and attacked by hackers; therefore, 3D model protection is a fundamental matter nowadays. In this paper, the proposed scheme will provide stringent security for the 3D models by implementing multiple levels of security with preserving the original dimensionality of the 3D model using the weight factor (w). The first level of security is achieved by applying a shuffling process for the vertices based on a key from random number generator (RNG), which provides good confusion. The second level is implemented by modifying the vertices values based on 3D keys from 3D Lorenz chaotic map, which provides good diffusion. The proposed scheme was applied on different 3D models varying in the vertices and faces number. The results illustrate that the proposed scheme deforms the entire 3D model based on Hausdorff distance (HD) approximately 100 after the encryption process, making it resist statistical attack. The scheme provides high security against brute force attack because it has a large key space equal to 10,105 and high security against deferential attack through secret key sensitivity using number of pixels change rate (NPCR) near to 99:6% and unified average changing intensity (UACI) near to 33:4%.
Proposed system for data security in distributed computing in using ‎triple data encryption standard and ‎Rivest Shamir ‎Adlemen
Authors: Shawkat Shihab A. , Tuama Bilal A. , Al_Barazanchi Israa
Published: September 2022

Cloud computing is considered a distributed computing paradigm in which resources ‎are ‎provided as services. In cloud computing, the ‎applications do not run ‎from a user’s personal computer but are run and stored on distributed ‎servers on the Internet. The ‎resources of the cloud infrastructures are shared on cloud ‎computing on the Internet in the open ‎environment. This increases the security problems in ‎security such as data confidentiality, data ‎integrity and data availability, so the solution of such ‎problems are conducted by adopting data ‎encryption is very important for securing users data. ‎In this paper, a comparative ‎study is done between the two security algorithms on a cloud ‎platform called eyeOS. From the ‎comparative study it was found that the Rivest Shamir ‎Adlemen ‎(3kRSA) algorithm ‎outperforms that triple data encryption standard (3DES) algorithm with ‎respect to the complexity, and output bytes. The main ‎drawback of the 3kRSA algorithm is its ‎computation time, while 3DES is faster than that ‎‎3kRSA. This is useful for storing large amounts of ‎data used in the cloud computing, the key ‎distribution and authentication of the asymmetric ‎encryption, speed, data integrity and data ‎confidentiality of the symmetric encryption are also ‎important also it enables to execute ‎required computations on this encrypted data.‎
Recommender systems: a novel approach based on singular value decomposition
Authors: Colace Francesco , Conte Dajana , Santo Massimo De , Lombardi Marco , Paternoster Beatrice , Santaniello Domenico , Valentino Carmine
Published: September 2022

Due to modern information and communication technologies (ICT), it is increasingly easier to exchange data and have new services available through the internet. However, the amount of data and services available increases the difficulty of finding what one needs. In this context, recommender systems represent the most promising solutions to overcome the problem of the so-called information overload, analyzing users' needs and preferences. Recommender systems (RS) are applied in different sectors with the same goal: to help people make choices based on an analysis of their behavior or users' similar characteristics or interests. This work presents a different approach for predicting ratings within the model-based collaborative filtering, which exploits singular value factorization. In particular, rating forecasts were generated through the characteristics related to users and items without the support of available ratings. The proposed method is evaluated through the MovieLens100K dataset performing an accuracy of 0.766 and 0.951 in terms of mean absolute error and root-mean-square error.
Best S-box amongst differently sized S-boxes based on the avalanche effect in ‎the advance encryption standard algorithm
Authors: Taher Hadeel Mohammed , Abd Al-Rahman Seddiq Qais , Shawkat Shihab A.
Published: September 2022

Substitution boxes are essential nonlinear modules that are popular in block ‎cipher algorithms. They ‎also play a significant role in the security area because of ‎their robustness to different linear ‎cryptanalysis. Each element of the state in a S-‎box is nonlinearly replaced using a lookup table. This ‎research presents the S-‎box, one of the fundamental parts of the advanced encryption standard ‎‎(AES) ‎algorithm. The S-box represents the confusion part in the AES. However, when ‎information ‎is shared between different devices in an authorized manner, the ‎algorithm should be able to ‎combine a sufficient number of confusion layers to ‎guarantee the avalanche effect (AE). ‎Subsequently, this research selects the best ‎S-box by comparing different sizes (4×4, 8×8, and ‎‎16×16) and measuring them ‎on the basis of the million-bit encryption. The AE is the main criterion ‎used in ‎choosing the best S-box. A robust and strong cryptography algorithm should be ‎able to ‎confirm the AEs. Results indicate that the 16×16 S-box with a 52% AE ‎ratio is the superior S-box
Hybrid information security system via combination of compression, cryptography, and image steganography
Authors: Awadh Wid Akeel , Alasady Ali Salah , Hamoud Alaa Khalaf
Published: September 2022

Today, the world is experiencing a new paradigm characterized by dynamism and rapid change due to revolutions that have gone through information and digital communication technologies, this raised many security and capacity concerns about information security transmitted via the Internet network. Cryptography and steganography are two of the most extensively that are used to ensure information security. Those techniques alone are not suitable for high security of information, so in this paper, we proposed a new system was proposed of hiding information within the image to optimize security and capacity. This system provides a sequence of steps by compressing the secret image using discrete wavelet transform (DWT) algorithm, then using the advanced encryption standard (AES) algorithm for encryption compressed data. The least significant bit (LSB) technique has been applied to hide the encrypted data. The results show that the proposed system is able to optimize the stego-image quality (PSNR value of 47.8 dB) and structural similarity index (SSIM value of 0.92). In addition, the results of the experiment proved that the combination of techniques maintains stego-image quality by 68%, improves system performance by 44%, and increases the size of secret data compared to using each technique alone. This study may contribute to solving the problem of the security and capacity of information when sent over the internet.
An automated essay evaluation system using natural language processing and sentiment analysi
Authors: Sadanand Vijaya Shetty , Guruvyas Kadagathur Raghavendra Rao , Patil Pranav Prashantha , Janardhan Acharya Jeevan , Gunakimath Suryakanth Sharvani
Published: September 2022

An automated essay evaluation system is a machine-based approach leveraging long short-term memory (LSTM) model to award grades to essays written in English language. natural language processing (NLP) is used to extract feature representations from the essays. The LSTM network learns from the extracted features and generates parameters for testing and validation. The main objectives of the research include proposing and training an LSTM model using a dataset of manually graded essays with scores. Sentiment analysis is performed to determine the sentiment of the essay as either positive, negative or neutral. The twitter sample dataset is used to build sentiment classifier that analyzes the sentiment based on the student’s approach towards a topic. Additionally, each essay is subjected to detection of syntactical errors as well as plagiarism check to detect the novelty of the essay. The overall grade is calculated based on the quality of the essay, the number of syntactic errors, the percentage of plagiarism found and sentiment of the essay. The corrected essay is provided as feedback to the students. This essay grading model has gained an average quadratic weighted kappa (QWK) score of 0.911 with 99.4% accuracy for the sentiment analysis classifier.
Machine learning and deep learning performance in classifying dyslexic children’s electroencephalogram during writing
Authors: Ahmad Zainuddin Ahmad Zuber , Mansor Wahidah , Lee Khuan Yoot , Mahmoodin Zulkifli
Published: September 2022

Dyslexia is a form of learning disability that causes a child to have difficulties in writing alphabets, reading words, and doing mathematics. Early identification of dyslexia is important to provide early intervention to improve learning disabilities. This study was carried out to differentiate EEG signals of poor dyslexic, capable dyslexic, and normal children during writing using machine learning and deep learning. three machine learning algorithms were studied: k-nearest neighbors (KNN), support vector machine (SVM), and extreme learning machine (ELM) with input features from coefficients of beta and theta band power extracted using discrete wavelet transform (DWT). As for the deep learning (DL) algorithm, long short-term memory (LSTM) architecture was employed. The kernel parameters of the classifiers were optimized to achieve high classification accuracy. Results showed that db8 achieved the greatest classification accuracy for all classifiers. Support vector machine with radial basis function kernel yields the highest accuracy which is 88% than other classifiers. The support vector machine with radial basis function kernel with db8 could be employed in determining the dyslexic children’s levels objectively during writing.
A novel application of artificial neural network for classifying agarwood essential oil quality
Authors: Mahabob Noratikah Zawani , Yusoff Zakiah Mohd , Mohd Amidon Aqib Fawwaz , Ismail Nurlaila , Taib Mohd Nasir
Published: September 2022

This work studies the agarwood oil classification into high and low quality by using two different techniques. Initially, the Forest Research Institute Malaysia (FRIM) and Universiti Malaysia Pahang (UMP) are where the sample preparation and compound extraction of agarwood oil is collected. The data collections were done from the previous researcher consists of 96 samples from seven significant agarwood oil compounds. The artificial neural network (ANN) and the proposed stepwise regression technique were used in this study. The stepwise regression was done the feature selection and successfully reduced agarwood oil compounds from seven to four. Then, the ANN technique was used to classify agarwood oil into high and low using input from seven and four compounds separately. The performance of ANN with different inputs is compared (ANN with seven inputs compared with ANN with four inputs). All the experimental work was performed using the MATLAB R2017b using the “patternet” implemented Levenberg Marquardt algorithm and ten hidden neurons. It was found that the ANN technique using seven compounds obtained the best performance according to high accuracy and lower mean square error (MSE) value. Finally, 1 hidden neuron in ANN with seven inputs selected as the best neuron for grading the agarwood oil compounds.
Acoustic event characterization for service robot using convolutional networks
Authors: Martinez Fernando , Martinez Fredy , Hernandez Cesar
Published: September 2022

This paper presents and discusses the creation of a sound event classification model using deep learning. In the design of service robots, it is necessary to include routines that improve the response of both the robot and the human being throughout the interaction. These types of tasks are critical when the robot is taking care of children, the elderly, or people in vulnerable situations. Certain dangerous situations are difficult to identify and assess by an autonomous system, and yet, the life of the users may depend on these robots. Acoustic signals correspond to events that can be detected at a great distance, are usually present in risky situations, and can be continuously sensed without incurring privacy risks. For the creation of the model, a customized database is structured with seven categories that allow to categorize a problem, and eventually allow the robot to provide the necessary help. These audio signals are processed to produce graphical representations consistent with human acoustic identification. These images are then used to train three convolutional models identified as high-performing in this type of problem. The three models are evaluated with specific metrics to identify the best-performing model. Finally, the results of this evaluation are discussed and analyzed.
Cascade networks model to predict the crude oil prices in Iraq
Authors: Al-Hilfi Suhair A. , Abd Ulkareem Naser Maysaa
Published: September 2022

Oil prices are inherently volatile, and they used to suffer from many fluctuations and changes. Therefore, oil prices prediction is the subject of many studies in the field, some researchers concentrated on the key factors that could influence the prediction accuracy, while the others focused on designing models that forecast the prices with high accuracy. To help the institutions and companies to hedge against any sudden changes and develop right decisions that support the global economy, in this project the concept of cascade networks model to predict the crude oil prices has been adopted, that can be considered relatively as new initiative in the field. The model is used to predict the Iraqi oil prices since as its commonly known that the economy in Iraq is totally depend on oil. Therefore, it is vital to develop a better perception about the crude oil price dynamics because its volatility can cause a sudden economic crisis.
Implementation of the C4.5 algorithm for micro, small, and medium enterprises classification
Authors: Lestari Sri , Yulmaini Yulmaini , Aswin Aswin , Sylvia Sylvia , Pratama Yan Aditiya , Sulyono Sulyono
Published: September 2022

The coronavirus disease-19 (COVID-19) pandemic has spread to various countries including Indonesia. Thus, implementing large-scale social restrictions (Bahasa: Pembatasan Sosial Berskala Besar (PSBB)) has resulted in the paralysis of the economy in Indonesia. including micro, small, and medium enterprises (MSMEs) have decreased turnover and even went out of business. The Department of Cooperatives and Small and Medium Enterprises (SMEs) in Pesawaran Regency, Lampung, oversees 3,808 MSMEs, whose development should be monitored as a basis for determining policies. However, there are problems in classifying MSMEs according to their categories because they have to check the existing data one by one, so it takes a long time. Therefore, this study proposed the C4.5 algorithm to solve this problem. In addition, this research compared with the naïve Bayes algorithm to find out which algorithm had a good performance and is suitable for this case. The results showed that 91% of MSMEs were included in the micro category, 8% was in a small category, and 1% was in the medium category. Based on the results, it explained that the C4.5 algorithm was bigger than naïve Bayes with a difference in the value of 3.79%. It had an accuracy value of 99.2%. Meanwhile, naive Bayes was 95.41%.
Intelligent grading of kaffir lime oil quality using non-linear support vector machine
Authors: Jak Jailani Nor Syahira , Muhammad Zuraida , Fazalul Rahiman Mohd Hezri , Taib Mohd Nasir
Published: September 2022

This paper presents kaffir lime oil quality grading using the intelligent system classification method, a non-linear support vector machine (NSVM). This method classifies the quality kaffir lime oil into two groups: high and low quality, based on their significant chemical compounds. The 90 data of kaffir lime oil were used in this project from high to low quality. The abundance (%) of significant chemical compounds will act as the input and high or low quality as an output. The 90 data will be divided into two sets: training and testing data sets with a ratio of 8:2. The radial basis function (RBF) optimization kernel parameters in NSVM. Using the implementation of MATLAB software version R2020a, all data and analysis work was performed automatically. The results showed that the NSVM model met all performance criteria for 100% accuracy, sensitivity, specificity, and precision.
Automatic video censoring system using deep learning
Authors: Verma Yash , Bhatia Madhulika , Tanwar Poonam , Bhatia Shaveta , Batra Mridula
Published: September 2022

Due to the extensive use of video-sharing platforms and services, the amount of such all kinds of content on the web has become massive. This abundance of information is a problem controlling the kind of content that may be present in such a video. More than telling if the content is suitable for children and sensitive people or not, figuring it out is also important what parts of it contains such content, for preserving parts that would be discarded in a simple broad analysis. To tackle this problem, a comparison was done for popular image deep learning models: MobileNetV2, Xception model, InceptionV3, VGG16, VGG19, ResNet101 and ResNet50 to seek the one that is most suitable for the required application. Also, a system is developed that would automatically censor inappropriate content such as violent scenes with the help of deep learning. The system uses a transfer learning mechanism using the VGG16 model. The experiments suggested that the model showed excellent performance for the automatic censoring application that could also be used in other similar applications.
Optimizing cybersecurity incident response decisions using deep reinforcement learning
Authors: Alturkistani Hilala , El-Affendi Mohammed A.
Published: September 2022

The main purpose of this paper is to explore and investigate the role of deep reinforcement learning (DRL) in optimizing the post-alert incident response process in security incident and event management (SIEM) systems. Although machine learning is used at multiple levels of SIEM systems, the last mile decision process is often ignored. Few papers reported efforts regarding the use of DRL to improve the post-alert decision and incident response processes. All the reported efforts applied only shallow (traditional) machine learning approaches to solve the problem. This paper explores the possibility of solving the problem using DRL approaches. The main attraction of DRL models is their ability to make accurate decisions based on live streams of data without the need for prior training, and they proved to be very successful in other fields of applications. Using standard datasets, a number of experiments have been conducted using different DRL configurations The results showed that DRL models can provide highly accurate decisions without the need for prior training.
A conceptual architecture for integrating software defined network and network virtualization with internet of things
Authors: Shamsan Ali Haider , Faridi Arman Rasool
Published: September 2022

Software defined network (SDN) and network function virtualization (NFV) are new paradigms and technologies of the network which support the best experience of providing functions and services, managing network traffic, and a new way of control. They support virtualization and separating data from control in network devices, as well as provide services in a software-based environment. Internet of things (IoT) is a heterogeneous network with a massive number of connected devices and objects. IoT should be integrated with such technologies for the purpose of providing the capabilities of dynamic reconfiguration with a high level of integration. This paper proposes a conceptual architecture for integrating software defined network (SDN) and NFV with IoT. The proposed work combines the three technologies together in one architecture. It also presents the previous works in this area and takes a look at the theoretical background of those technologies in order to give a complete view of proposed work.
Internet of things based real-time coronavirus 2019 disease patient health monitoring system
Authors: Ejin Abraham Ninian , Yew Hoe Tung , Mamat Mazlina , Wong Farrah , Chekima Ali , Chung Seng Kheau
Published: September 2022

The coronavirus disease (COVID-19) outbreak has led to many infected worldwide and has become a global crisis. COVID-19 manifests in the form of shortness of breath, coughing and fever. More people are getting infected and healthcare systems worldwide are overwhelmed as healthcare workers become exhausted and infected. Thus, remote monitoring for COVID-19 patients is required. An internet of things (IoT) based real-time health monitoring system for COVID-19 patients was proposed. It features monitoring of five physiological parameters, namely electrocardiogram (ECG), heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2) and body temperature. These vitals are processed by the main controller and transmitted to the cloud for storage. Healthcare professionals can read real-time patient vitals on the web-based dashboard which is equipped with an alert service. The proposed system was able to transmit and display all parameters in real-time accurately without any packet loss or transmission errors. The accuracy of body temperature readings, RR, SpO2 and HR, is up to 99.7%, 100%, 97.97% and 98.34%, respectively. Alerts were successfully sent when the parameters reached unsafe levels. With the proposed system, healthcare professionals can remotely monitor COVID-19 patients with greater ease, lessen their exposure to the pathogen, and improve patient monitoring.
An improved light gradient boosting machine algorithm based on swarm algorithms for predicting loan default of peer-to-peer lending
Authors: Muslim Much Aziz , Dasril Yosza , Sam'an Muhammad , Ifriza Yahya Nur
Published: September 2022

Internet finance and big data technology are booming in the world. The launch of peer to peer (P2P) lending platforms is a sign and a great opportunity for entrepreneurs to easily increase their capital injection. However, this great opportunity has a high risk of impacting the sustainability and security development of the platform. One way to minimize loan risk is to predict the possibility of loan default. Hence, this study aims to find the best predictive model for predicting loan default of P2P Lending Club dataset. An improved light gradient boosting machine (LightGBM) via features selection by using swarm algorithms i.e. Ant colony optimization (ACO) and bee colony optimization (BCO) to the prediction analysis process. The best feature selection process is selected 6 out of 18 features. The synthetic minority oversampling technique (SMOTE) method is also provided to solve the unbalance class problem in the dataset, then a series of operations such as data cleaning and dimension reduction are performed. The experimental results prove that the LightGBM algorithm has been successfully improved. This success is shown by the prediction accuracy of LightGBM+ACO is 95.64%, LighGBM+BCO is 94.70% and LightGBM is 94.38%. This success also demonstrates outstanding performance in predicting loan default and strong generalizations.
Missing values imputation in Arabic datasets using enhanced robust association rules
Authors: Salem Awsan , Akmar Emran Nurul , Kamilah Muda Azah , Sahri Zahriah , Ali Abdulrazzak
Published: September 2022

Missing value (MV) is one form of data completeness problem in massive datasets. To deal with missing values, data imputation methods were proposed with the aim to improve the completeness of the datasets concerned. Data imputation's accuracy is a common indicator of a data imputation technique's efficiency. However, the efficiency of data imputation can be affected by the nature of the language in which the dataset is written. To overcome this problem, it is necessary to normalize the data, especially in non-Latin languages such as the Arabic language. This paper proposes a method that will address the challenge inherent in Arabic datasets by extending the enhanced robust association rules (ERAR) method with Arabic detection and correction functions. Iterative and Decision Tree methods were used to evaluate the proposed method in an experiment. Experiment results show that the proposed method offers a higher data imputation accuracy than the Iterative and Decision Tree methods.